system

Abstract

We provide the system. [Solution] Means for receiving the user's current location and destination information, A means for selecting the optimal driver by analyzing the user's past usage history and preference information, A means of providing selected drivers with information on optimal routes and high-demand areas, A means of calculating and presenting the optimal price to the user in real time, A means of matching selected drivers with users, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, 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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional car - sharing service, problems include the long waiting time faced by users, the opacity of fares, and the lack of matching efficiency due to the imbalance between demand and supply. Also, on the driver side, there is a problem that it is difficult to understand optimal operation and demand. As a result, the satisfaction of users and drivers may decrease, and the quality of the entire service may be impaired.

Means for Solving the Problems

[0005] This invention provides a system in which an AI agent quickly selects the most suitable driver based on the user's travel needs. It analyzes the user's current location and destination, past usage history, and preferences to calculate and present the optimal fare in real time. Furthermore, it reduces waiting times by predicting demand in each area and optimizing driver allocation. In addition, it provides drivers with optimal routes and information on high-demand areas, improving profitability. These features enhance satisfaction for both users and drivers, thereby improving the quality of ride-hailing services.

[0006] "Means for receiving the user's current location and destination information" refers to a function that allows the system to acquire location information entered by the user via a smart device or similar device.

[0007] "A means of selecting the optimal driver by analyzing the user's past usage history and preference information" refers to a function that analyzes the user's past service usage records and preferences and determines the most suitable driver based on that.

[0008] "Means of providing selected drivers with optimal routes and information on high-demand areas" refers to a function that provides drivers with real-time information on the most efficient routes to reach their destinations and information on high-demand areas.

[0009] "A means of calculating and presenting the optimal price to the user in real time" refers to a function that instantly calculates and informs the user of the appropriate price based on the supply and demand situation at that moment.

[0010] "A means of matching selected drivers with users" refers to a function that automatically assigns a driver who meets the user's requirements and arranges the ride appropriately.

[0011] "Using AI technology to predict demand in each area in real time and optimize driver deployment" refers to a process that utilizes artificial intelligence to predict demand in specific areas over time and then effectively deploys drivers based on that prediction.

[0012] "Users evaluate drivers, and their feedback is used in future matching processes" means that after the service ends, users evaluate the driver's performance, and this evaluation is used to improve future driver matching. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disk (e.g., hard disk), or magnetic tape, etc.

[0019] 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).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention relates to a ride-hailing service system based on the user's transportation needs. The system uses an AI agent to select the most suitable driver, enabling rapid matching and fare quotes. The following describes specific embodiments of the system.

[0035] System Configuration

[0036] 1. The terminal functions as a smart device and provides a means for the user to input their current location, destination, and preferences (such as vehicle type and driver rating). This information is transmitted as a digital signal to a server within the system.

[0037] 2. The server is a central system that processes the received data. The server is equipped with an analysis module that includes an AI agent, which selects the optimal driver based on the user's history and preferences.

[0038] 3. The server then uses the information entered by the user and the results of the AI ​​analysis to activate the pricing engine. This engine considers real-time supply and demand data to calculate an appropriate price for the user.

[0039] 4. The terminals used by drivers work in conjunction with the server to provide drivers with information on optimal routes and high-demand areas. This information is based on demand forecasts generated by an AI agent.

[0040] 5. Once the user approves the provided ride conditions (fare, driver, estimated arrival time, etc.), the server sends the pickup location information to the driver, and the actual dispatch begins.

[0041] 6. After the dispatch is complete, the user rates the driver through the application. This rate is stored on the server and used as feedback for future matching algorithms.

[0042] Specific example

[0043] For example, if a user enters "I would like a ride from my office to the airport," the terminal sends that geographical information to the server. The server's AI agent selects the most suitable driver for that user based on past history and rating data. Next, the fare is calculated based on real-time traffic conditions, and the terminal displays the result to the user. After confirmation via the mobile app, the finally selected driver heads to the pickup location.

[0044] This invention's system enables efficient vehicle dispatching and provides a highly satisfying service to both users and drivers.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] The user accesses an application on their smart device and enters their current location, destination, and preferred criteria (such as vehicle type and driver rating). This generates request data.

[0048] Step 2:

[0049] The device sends this request data to the server. This data is transferred either as text or as structured data via an API.

[0050] Step 3:

[0051] The server analyzes the received request data and searches the database, taking into account the user's past usage history and preferences. This generates a list of optimal driver candidates.

[0052] Step 4:

[0053] An AI agent on the server uses real-time traffic conditions and geographical information to predict demand in each area. Based on these results, it optimizes driver deployment and selects the most suitable driver.

[0054] Step 5:

[0055] The server provides the selected driver with information on optimal routes and high-demand areas, and notifies the driver. The driver's terminal receives this information and begins moving towards the pickup location.

[0056] Step 6:

[0057] The server uses real-time demand data to calculate the appropriate price to present to the user. This pricing information is sent to the terminal and presented to the user in advance.

[0058] Step 7:

[0059] Once the user accepts the presented terms, the dispatch officially begins. The driver heads to the customer's pickup location and starts moving.

[0060] Step 8:

[0061] After the transfer is complete, the user evaluates the driver through the application. This evaluation is stored on the server and used for future driver matching.

[0062] (Example 1)

[0063] 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."

[0064] In today's transportation landscape, rapid and accurate vehicle allocation is essential. However, conventional systems often fail to respond quickly to fluctuations in demand, leading to decreased customer satisfaction. Furthermore, insufficient driver matching quality has resulted in reduced travel efficiency. Moreover, the lack of driver feedback being utilized in the matching process means that opportunities for improvement are lost.

[0065] 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.

[0066] In this invention, the server includes a device for receiving the user's geographical location information and destination information, a device for selecting the optimal driver by analyzing the user's past usage records and preference information, and a device for calculating and displaying the optimal fare to the user, taking time information into consideration. This enables the provision of an optimal ride-hailing service that responds to demand in real time and high-quality matching with drivers.

[0067] "Geographic location information" refers to data that indicates the specific physical location where the user is currently located.

[0068] "Destination information" refers to data that indicates the location of the destination the user is aiming for.

[0069] "Past usage records" refer to information about the history of services that a user has used in the past.

[0070] "Preference information" refers to information that indicates the characteristics of drivers that users prefer and their personal preferences regarding vehicles.

[0071] "Driver" refers to an individual or person who provides transportation services in response to the user's request.

[0072] "Data processing technology" refers to the technical means of processing large amounts of information quickly and efficiently.

[0073] A "terminal" is an electronic device used by users to input information or receive results.

[0074] The "analysis process" is the procedure for selecting the optimal driver based on the received data.

[0075] "Feedback" refers to data that shows the evaluations and opinions that users provide regarding the quality of a service.

[0076] This invention is a ride-hailing service system that enables users to travel smoothly. Users use a device such as a smartphone or tablet to launch an application. The application provides an interface for inputting geographical location information and destination information. This allows users to input their departure point, destination, and preferred vehicle and driver conditions.

[0077] The terminal transmits the input information as a digital signal to a server within the system. The server receives this information using high-performance data processing technology and uses an AI-powered analysis module to select the most suitable driver based on the user's past usage history and preferences. This process aims to enable efficient driver selection and provide users with high-quality ride-hailing services.

[0078] In terms of specific hardware, smart devices function as terminals, and a computing system including a central processing unit operates as a server. The software running on the server includes an AI agent and a fare calculation engine. The AI ​​agent uses a generative AI model to optimize the matching of users and drivers, and the fare calculation engine considers real-time supply and demand conditions to present users with appropriate fares.

[0079] As a concrete example, if a user enters "I would like a ride from my office to the airport," the terminal sends this information to the server. The server uses an AI agent to select the most suitable driver for the user and calculates the fare based on real-time traffic information. The result is then presented to the user through the terminal. Once the most suitable driver is selected and approved, the ride-hailing service begins. An example of a prompt to be entered into the generating AI model would be: "Please arrange the most suitable ride from my current location (office) to the airport. I would like a sedan, and please select a driver with a high past rating."

[0080] By implementing this invention, it is possible to provide efficient and highly satisfying mobility services for both users and drivers.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] Users access the application using a smart device and input geographical location information, destination information, and desired vehicle and driver conditions. This input data constitutes the user's travel needs. The device converts this information into digital signals and prepares them for transmission to a server within the system.

[0084] Step 2:

[0085] The terminal transmits the input information, converted as a digital signal, to a server within the system. This input consists of user requests such as geographical location information, destination information, and desired conditions. The server receives this information as output, preparing it for the next processing stage.

[0086] Step 3:

[0087] The server compares the received user information with past usage records and preference information stored in the database, and performs analysis using a generating AI model. This process selects a driver based on past evaluations and preferences. The input is user information and historical data, and the output is the result of selecting the optimal driver.

[0088] Step 4:

[0089] The server activates the fare calculation engine and calculates fares taking into account real-time traffic conditions and the balance of supply and demand. Inputs include traffic data and supply and demand information, and the output is the appropriate fare to be presented to the user. This calculation is adapted to current market conditions.

[0090] Step 5:

[0091] The server transmits the selected driver information and calculated fare to the terminal and presents it to the user. The input is the driver and fare information, and the output is displayed on the user's screen. The user reviews the presented conditions and chooses to accept or reject them.

[0092] Step 6:

[0093] Once the user approves the presented conditions, the server sends pickup location information to the driver's terminal based on the approved information. The input is the approved matching information, and the output is a work instruction for the driver. Based on this information, the driver begins their journey to the user's location.

[0094] Step 7:

[0095] After a vehicle assignment is complete, the user rates the driver through the application. The rating information is sent to the server and used as feedback in subsequent matching processes. The input is the user's rating data, and the output is the saved rating as feedback.

[0096] (Application Example 1)

[0097] 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."

[0098] In food delivery services, challenges exist in selecting the optimal delivery professional to match individual customer preferences and in providing quick and efficient pricing. Furthermore, there is a need to improve service quality through real-time demand forecasting and optimal delivery personnel deployment.

[0099] 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.

[0100] In this invention, the server includes means for receiving the user's current location and destination information, means for analyzing the user's past usage history and preference information to select the most suitable delivery person, and means for providing the selected delivery person with information on the optimal travel route and high-demand areas. This enables rapid matching of delivery professionals with the individual user's needs and pricing.

[0101] "User" refers to an individual or group that receives a product or service.

[0102] "Current location" refers to the geographical location where the user is located in real time.

[0103] "Destination" refers to the geographical location that the user wishes to reach.

[0104] "Usage history information" refers to data related to transactions and service usage that a user has performed in the past.

[0105] "Preference information" refers to data about individual preferences based on choices and evaluations that users have made in the past.

[0106] A "delivery person" refers to a person who receives an order and delivers the goods to the designated destination.

[0107] An "optimal travel route" refers to a route that is set up to minimize the time and distance required for a delivery person to reach their destination.

[0108] "High-demand area information" refers to data on situations where orders and delivery requests are concentrated in specific regions.

[0109] "Real-time" refers to a situation where transmission, processing, or response occurs instantly.

[0110] "Fees" refer to the monetary compensation that a user pays for a service or product provided.

[0111] "Matching" refers to the process of finding and connecting the optimal combination of users and delivery personnel.

[0112] The system that implements this application consists mainly of the user's smart device (terminal) and a central server. When a user wants food delivery, they place an order using their terminal. At this time, the user's current location information, destination information, past usage history, and preference information are transmitted to the server as digital data.

[0113] The server utilizes AI agents to select the optimal delivery person based on past evaluation data, real-time road conditions, and order demand forecasts. AI agents employ technologies such as TENSORFLOW® and PyTorch. Furthermore, a dedicated engine operates to analyze real-time data for calculating delivery fees and estimated delivery times.

[0114] Selected delivery drivers are provided with information on optimal routes and high-demand areas via smart devices. This allows drivers to efficiently deliver goods to their destinations. After delivery is complete, users rate the delivery driver on their device, and this feedback is used in future matching processes.

[0115] For example, if a customer orders a pizza on a weekend evening, the system quickly selects the most suitable pizza delivery person and arranges for the order to be delivered within 30 minutes. Once the order is confirmed, the delivery person receives real-time information on the shortest route and executes the delivery smoothly.

[0116] An example of a prompt message is: "Select the best food delivery driver for the address specified by the user. Calculate the estimated arrival time and delivery fee, taking into account the driver's past ratings and current traffic conditions."

[0117] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0118] Step 1:

[0119] The user places a food delivery order using a terminal. In this step, the user enters their current location, destination, and order details. The input data is collected by the terminal and sent to the server as a digital signal. The output is the transmission of data to the server.

[0120] Step 2:

[0121] The server receives the transmitted data and uses an AI agent to analyze the user's past usage history and preferences. This analysis generates initial data for selecting the most suitable delivery person. The input is the user's past evaluation data, and the output is a list of candidate delivery people.

[0122] Step 3:

[0123] The server analyzes traffic conditions and order demand forecasts in real time, integrates the results with user information, and selects the most suitable delivery person for each delivery. This process uses data analysis algorithms for optimization. The inputs are traffic data and demand data, and the output is the selected delivery person.

[0124] Step 4:

[0125] The server notifies selected delivery personnel of the optimal travel route and information on high-demand areas. This notification enables delivery personnel to make deliveries efficiently. The input is the result of the delivery personnel selection, and the output is the information provided to the delivery personnel.

[0126] Step 5:

[0127] The user confirms the displayed price and estimated arrival time via the terminal and confirms the order. The input is the information displayed by the server, and the output is the user's confirmation.

[0128] Step 6:

[0129] A delivery person delivers the goods from the pickup point to the destination, completing the delivery. After delivery is complete, the user rates the delivery person on their device. This rating data is sent to the server and used in future matching processes. The input is the user's rating, and the output is feedback to the server.

[0130] 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.

[0131] This invention combines emotion recognition technology with a ride-hailing service system designed to meet users' transportation needs. The system utilizes an AI agent to provide an optimal ride-hailing experience that takes into account the user's emotional state.

[0132] System Configuration

[0133] 1. The terminal is a smart device used by the user to send requests and collects data to analyze the user's emotions (e.g., voice tone and input speed) in addition to basic location information. This emotional information is then sent to the server as part of the request data.

[0134] 2. Upon receiving this emotion information, the server activates the emotion engine and evaluates the user's current emotional state. The emotion engine combines natural language processing and speech analysis technologies to recognize states such as stress, joy, and anger.

[0135] 3. The server uses the results obtained from the emotion engine to consider the user's emotional state when the AI ​​agent selects the most suitable driver candidate. For example, if the user is feeling stressed, a driver with particularly good customer service skills will be given priority.

[0136] 4. The optimal pricing offered to users may also be adjusted to take their emotional state into consideration. For example, users experiencing stress may be offered special offers to minimize price fluctuations.

[0137] 5. Drivers' devices receive real-time notifications of tips for providing considerate service to passengers before they board. This allows drivers to respond more appropriately to passengers.

[0138] 6. After the journey is complete, the user provides feedback on the driver and the overall service via the application. This feedback is analyzed by the emotion engine and used to improve the service in the future.

[0139] Specific example

[0140] For example, if a user is in a hurry and requests to be "arrived as quickly as possible," the server recognizes the user's urgency based on their voice tone and the speed of their message. Based on this, the server selects a driver who can arrive immediately, providing a fast service that meets the user's needs.

[0141] This invention's system enables flexible ride-hailing services that are sensitive to the user's feelings, further improving the user experience.

[0142] The following describes the processing flow.

[0143] Step 1:

[0144] The user opens an application on their smart device and enters a ride request. This includes their current location, destination, preferences (e.g., vehicle type and driver rating), as well as sentiment data entered via voice or message.

[0145] Step 2:

[0146] The device prepares the request data and sends location information, usage history, and sentiment data to the server all at once. The sentiment data consists of analysis of voice tone and text input speed.

[0147] Step 3:

[0148] Based on the received data, the server's AI agent analyzes the user's past usage history and preferences. Simultaneously, an emotion engine is activated, analyzing voice and text data to estimate the user's emotional state. Emotions are categorized into types such as stress, relief, and anxiety.

[0149] Step 4:

[0150] Based on the output of the emotion engine, the server's AI agent selects the most appropriate driver. If the user is experiencing stress, the matching process prioritizes drivers with high service ratings.

[0151] Step 5:

[0152] The server calculates the optimal fare based on real-time traffic conditions and supply-demand balance, while also considering the user's emotional state, and presents it to the user in the most reassuring way. This result is transmitted to the terminal in real time and displayed to the user.

[0153] Step 6:

[0154] The driver's terminal receives service delivery hints from the server based on the user's emotional information. The driver uses this information to adjust the service during the ride.

[0155] Step 7:

[0156] After the ride is complete, users provide feedback on the driver and the overall service through the application. This feedback is re-analyzed by an emotion engine and used to improve future matching and service.

[0157] (Example 2)

[0158] 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".

[0159] Traditional ride-hailing service systems have struggled to provide a high-quality customer experience without considering the emotional state of users. In particular, when users' emotional states, such as stress and dissatisfaction, affect the service experience, a more flexible and emotionally resonant service is needed. There is a need for methods to address these challenges and improve user satisfaction.

[0160] 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.

[0161] In this invention, the server includes means for receiving the user's current location and destination location information, means for evaluating the user's emotional state from voice and input data, and means for selecting a driver based on the emotional evaluation and adapting to the service provision. This makes it possible to provide considerate service that responds to the user's emotions.

[0162] "Current location information" refers to data that indicates the real-time location of the device being used by the user.

[0163] "Destination location information" is data that indicates the location of the point the user wants to reach.

[0164] "Usage history information" refers to records of past service usage by users, including date and time, route, and driver information.

[0165] "Preference information" refers to data about individual preferences based on the conditions and characteristics of services that users like, as well as past evaluations.

[0166] A "driver" refers to a person who is responsible for transporting users to their destination when providing a ride-hailing service.

[0167] The "optimal route" is the route chosen by the driver to transport passengers to their destination quickly and efficiently.

[0168] "High-demand local information" refers to information about areas where many users need the service at a specific time and place.

[0169] "Emotional state" refers to the user's psychological and emotional condition and is evaluated by changes in voice tone and input speed.

[0170] "Voice and input data" refers to information about voice and text input acquired by the user's device, from which emotional states can be interpreted.

[0171] "Emotional assessment" is the process of analyzing and understanding users' emotions based on collected data.

[0172] "Matching" refers to the process of selecting and connecting the driver best suited to the user's needs.

[0173] The ride-hailing service system of this invention consists of a user, a terminal, and a server. First, the user requests a ride using a smart device. The user's terminal collects emotional information by analyzing location information, voice tone, and input speed. This collected data is transmitted to the server.

[0174] The server receives this data and uses an emotion engine to evaluate the user's emotional state. The emotion engine uses natural language processing and speech analysis technologies to specifically assess the user's emotions. Based on this evaluation, the AI ​​agent selects a driver. For example, if the user is evaluated as feeling stressed, a driver with excellent customer service skills may be selected. In addition, the driver is notified of virtual service hints, enabling them to provide service that takes the user's emotions into consideration.

[0175] As a concrete example, if a user is in a hurry and requests by voice, "I want to arrive on time," the server will recognize the urgency from the tone of voice and the speed of the message. This will then select the driver who can respond most quickly.

[0176] In this system, the generative AI model enables the delivery of services based on user emotions, providing a flexible and personalized ride-hailing experience.

[0177] Example of a prompt

[0178] "Please tell me what kind of considerations are possible when a user is feeling anxious."

[0179] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0180] Step 1:

[0181] The terminal receives ride-hailing requests from users, including destinations and desired times. In addition, it acquires data to infer the user's emotions, such as the speed of voice and text input and their tone of voice. This entire set of data is then input to the server.

[0182] Step 2:

[0183] The server receives request data and associated emotion data sent from the terminal. The received data is passed to the emotion engine, which analyzes the user's emotional state using natural language processing and speech analysis techniques. This data processing results in the output of emotional states such as stress, joy, and anger.

[0184] Step 3:

[0185] The server uses an AI agent based on the analysis results of the emotion engine to select the appropriate driver. The selection process considers the evaluated emotional state, the driver's current location, estimated arrival time, and past user ratings to output information on the most suitable driver.

[0186] Step 4:

[0187] The server notifies the selected driver's terminal in real time with hints for providing service that take into account the user's emotional state. For example, it might send a message such as, "The user is feeling stressed, so try to respond calmly." This allows the driver to follow the guidance and respond more appropriately.

[0188] Step 5:

[0189] After the journey is complete, the user provides feedback on the driver and the service provided. This feedback is sent to the server and analyzed again by the sentiment engine. The analyzed feedback becomes important data for improving the service in the future.

[0190] (Application Example 2)

[0191] 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".

[0192] Traditional ride-hailing services did not take into account the emotional state of users, resulting in a uniform service delivery. Therefore, there were limitations in providing a satisfactory ride-hailing experience when users were stressed or seeking relaxation. Furthermore, the matching of drivers and fare presentations did not take users' emotions into consideration, potentially leading to decreased customer satisfaction.

[0193] 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.

[0194] In this invention, the server includes means for receiving the user's current emotional state, means for providing the driver and the user with the optimal route and high-demand area information in real time based on the user's emotional state, and means for calculating and presenting a fare adjusted to the user's emotional state. This makes it possible to provide a flexible ride-hailing service that is attentive to the user's emotions.

[0195] "Means for receiving the user's current emotional state" refers to technology that analyzes voice data and input speed obtained from the user to detect and receive their emotional state in real time.

[0196] "Means for receiving the user's current location and destination information" refers to technology for collecting geographical information of the starting point and destination specified by the user and incorporating it into the system.

[0197] "A means of selecting the optimal driver by analyzing the user's past usage history and preference information" refers to a technology that analyzes past usage history and personal preference data to select the driver best suited to the user.

[0198] "Means of providing drivers and users with real-time optimal routes and information on high-demand areas" refers to a function that provides drivers and users with real-time information on optimal routes and areas with high demand, based on current traffic conditions and demand forecast data.

[0199] "A means of calculating and presenting a fee adjusted to the user's emotional state" refers to a technology that, based on the results of an emotional analysis of the user, sets a fee that takes into account stress reduction and the provision of a sense of security, and then presents this to the user.

[0200] "Means for matching selected drivers with users" refers to a method of efficiently providing services by connecting the most suitable drivers and users based on analyzed information.

[0201] The embodiments for carrying out this invention primarily relate to a server, a user's smart device, and an autonomous vehicle. The server receives voice data and input speed from the user's smart device and detects the user's current emotional state in real time using emotion analysis technology. This involves converting speech to text using Google® Cloud Speech-to-Text API and then performing emotion analysis using IBM Watson® Natural Language Understanding.

[0202] The user's smart device provides current location and destination information and transmits this information to the server. Furthermore, based on past usage history and preference information, the server analyzes and selects the most suitable driver. The server also utilizes AI models to predict demand in real time and provides drivers and users with the most efficient routes and information on high-demand areas.

[0203] Another important element of this program is fare adjustment based on the user's emotional state. This ensures that the most appropriate service is provided to the user. For example, if a family is going on a weekend trip to the beach and the system determines that they are feeling stressed at the start of the trip, it will play relaxing music or select a route with less traffic.

[0204] An example of a prompt to input into the generating AI model would be, "The user is feeling a little stressed at the start of their trip. Based on the emotion recognition results, how would you adjust the environment to help them relax?" This question asks about the quality of service required based on a specific emotional state.

[0205] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0206] Step 1:

[0207] The user's device acquires the user's voice data and input speed. This data is sent to the server as input. The server uses the Google Cloud Speech-to-Text API to convert the voice data into text and prepares it for analyzing the user's emotional state.

[0208] Step 2:

[0209] The server uses IBM Watson Natural Language Understanding to perform sentiment analysis on text obtained from speech. This analysis detects emotions such as stress, joy, and anger. The analysis results are generated as sentiment tags, which serve as input for the next step.

[0210] Step 3:

[0211] The server receives the user's current location and destination information. This information is combined with past usage history and preference data, and a generative AI model is used to select the most suitable driver. The output is a list of potential optimal drivers.

[0212] Step 4:

[0213] The server provides drivers and users with real-time information on optimal routes and high-demand areas. This decision-making process utilizes AI technology to acquire demand forecasting data and evaluate the convenience of each route. The output provides real-time driving routes and related information.

[0214] Step 5:

[0215] The server calculates a fee adjusted for the user based on their emotional state. If the emotional state indicates stress, adjustments such as discounts are applied. This calculates the optimal fee for the user, and this information is presented to the user. The output is the adjusted fee information.

[0216] Step 6:

[0217] The server matches selected drivers with users. This process utilizes previously obtained sentiment tags and historical information, with the driver's response skills being the evaluation criterion. The selection results are output, and a match is confirmed.

[0218] Step 7:

[0219] After the ride-hailing experience ends, users send feedback about the driver and service to the server via their device. The server analyzes this feedback to improve future services and stores it as new data. The input is feedback information, and the output is trend analysis data as a result of the analysis.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] [Second Embodiment]

[0224] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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).

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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".

[0236] This invention relates to a ride-hailing service system based on the user's transportation needs. The system uses an AI agent to select the most suitable driver, enabling rapid matching and fare quotes. The following describes specific embodiments of the system.

[0237] System Configuration

[0238] 1. The terminal functions as a smart device and provides a means for the user to input their current location, destination, and preferences (such as vehicle type and driver rating). This information is transmitted as a digital signal to a server within the system.

[0239] 2. The server is a central system that processes the received data. The server is equipped with an analysis module that includes an AI agent, which selects the optimal driver based on the user's history and preferences.

[0240] 3. The server then uses the information entered by the user and the results of the AI ​​analysis to activate the pricing engine. This engine considers real-time supply and demand data to calculate an appropriate price for the user.

[0241] 4. The terminals used by drivers work in conjunction with the server to provide drivers with information on optimal routes and high-demand areas. This information is based on demand forecasts generated by an AI agent.

[0242] 5. Once the user approves the provided ride conditions (fare, driver, estimated arrival time, etc.), the server sends the pickup location information to the driver, and the actual dispatch begins.

[0243] 6. After the dispatch is complete, the user rates the driver through the application. This rate is stored on the server and used as feedback for future matching algorithms.

[0244] Specific example

[0245] For example, if a user enters "I would like a ride from my office to the airport," the terminal sends that geographical information to the server. The server's AI agent selects the most suitable driver for that user based on past history and rating data. Next, the fare is calculated based on real-time traffic conditions, and the terminal displays the result to the user. After confirmation via the mobile app, the finally selected driver heads to the pickup location.

[0246] This invention's system enables efficient vehicle dispatching and provides a highly satisfying service to both users and drivers.

[0247] The following describes the processing flow.

[0248] Step 1:

[0249] The user accesses an application on their smart device and enters their current location, destination, and preferred criteria (such as vehicle type and driver rating). This generates request data.

[0250] Step 2:

[0251] The device sends this request data to the server. This data is transferred either as text or as structured data via an API.

[0252] Step 3:

[0253] The server analyzes the received request data and searches the database, taking into account the user's past usage history and preferences. This generates a list of optimal driver candidates.

[0254] Step 4:

[0255] An AI agent on the server uses real-time traffic conditions and geographical information to predict demand in each area. Based on these results, it optimizes driver deployment and selects the most suitable driver.

[0256] Step 5:

[0257] The server provides the selected driver with information on optimal routes and high-demand areas, and notifies the driver. The driver's terminal receives this information and begins moving towards the pickup location.

[0258] Step 6:

[0259] The server uses real-time demand data to calculate the appropriate price to present to the user. This pricing information is sent to the terminal and presented to the user in advance.

[0260] Step 7:

[0261] Once the user accepts the presented terms, the dispatch officially begins. The driver heads to the customer's pickup location and starts moving.

[0262] Step 8:

[0263] After the transfer is complete, the user evaluates the driver through the application. This evaluation is stored on the server and used for future driver matching.

[0264] (Example 1)

[0265] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0266] In today's transportation landscape, rapid and accurate vehicle allocation is essential. However, conventional systems often fail to respond quickly to fluctuations in demand, leading to decreased customer satisfaction. Furthermore, insufficient driver matching quality has resulted in reduced travel efficiency. Moreover, the lack of driver feedback being utilized in the matching process means that opportunities for improvement are lost.

[0267] 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.

[0268] In this invention, the server includes a device for receiving the user's geographical location information and destination information, a device for selecting the optimal driver by analyzing the user's past usage records and preference information, and a device for calculating and displaying the optimal fare to the user, taking time information into consideration. This enables the provision of an optimal ride-hailing service that responds to demand in real time and high-quality matching with drivers.

[0269] "Geographic location information" refers to data that indicates the specific physical location where the user is currently located.

[0270] "Destination information" refers to data that indicates the location of the destination the user is aiming for.

[0271] "Past usage records" refer to information about the history of services that a user has used in the past.

[0272] "Preference information" refers to information that indicates the characteristics of drivers that users prefer and their personal preferences regarding vehicles.

[0273] "Driver" refers to an individual or person who provides transportation services in response to the user's request.

[0274] "Data processing technology" refers to the technical means of processing large amounts of information quickly and efficiently.

[0275] A "terminal" is an electronic device used by users to input information or receive results.

[0276] The "analysis process" is the procedure for selecting the optimal driver based on the received data.

[0277] "Feedback" refers to data that shows the evaluations and opinions that users provide regarding the quality of a service.

[0278] This invention is a ride-hailing service system that enables users to travel smoothly. Users use a device such as a smartphone or tablet to launch an application. The application provides an interface for inputting geographical location information and destination information. This allows users to input their departure point, destination, and preferred vehicle and driver conditions.

[0279] The terminal transmits the input information as a digital signal to the server within the system. The server receives this information using high-performance data processing technology and selects an optimal driver based on the user's past usage records and preference information using an analysis module that utilizes artificial intelligence. This process aims to enable efficient driver selection and provide users with a high-quality carpooling service.

[0280] As specific hardware, a smart device functions as the terminal, and a computer system including a central processing unit operates as the server. The software operating on the server includes an AI agent and a fare calculation engine. The AI agent uses a generative AI model to optimize the matching between users and drivers, and the fare calculation engine presents an appropriate fare to the user considering real-time demand and supply situations.

[0281] As a specific example, when a user inputs "I want a carpool from the office to the airport", the terminal transmits that information to the server. The server uses the AI agent to select an optimal driver for the user and calculates the fare based on real-time traffic information. The result is presented to the user through the terminal. If an optimal driver is selected and approved, the carpooling service is started. Examples of prompt texts input to the generative AI model include inputs in the form of "Please arrange an optimal carpool from the current location (the office) to the airport. The desired vehicle type is a sedan, and please select a driver with a high past evaluation."

[0282] By implementing this invention, it is possible to provide an efficient and highly satisfactory mobility service for both users and drivers.

[0283] The flow of the specific process in Example 1 will be described using FIG. 11.

[0284] Step 1:

[0285] The user accesses the application using a smart device and inputs geographical location information, destination information, and the conditions of the desired vehicle and driver. These input data constitute the user's travel needs. The terminal converts this information into digital signals and prepares to transmit them to the server within the system.

[0286] Step 2:

[0287] The terminal transmits the input information converted into digital signals to the server within the system. The input here is the user's requests such as geographical location information, destination information, and desired conditions. As output, the server receives this information and prepares to proceed to the next processing stage.

[0288] Step 3:

[0289] The server matches the received user information with past usage records and preference information stored in the database and performs analysis using a generated AI model. Through this process, a driver is selected based on past evaluations and preferences. The input is user information and historical data, and the output is the selection result of the optimal driver.

[0290] Step 4:

[0291] The server activates the fare calculation engine and calculates the fare considering real-time traffic conditions and the balance of demand and supply. The input includes traffic data and demand-supply information, and the output is the appropriate fare to be presented to the user. This calculation adapts to the current market conditions.

[0292] Step 5:

[0293] The server transmits the selected driver information and the calculated fare to the terminal and presents them to the user. The input is the driver and fare information, and the output is that it is displayed on the user's screen. The user checks the presented conditions and selects an action of approval or rejection.

[0294] Step 6:

[0295] Once the user approves the presented conditions, the server sends pickup location information to the driver's terminal based on the approved information. The input is the approved matching information, and the output is a work instruction for the driver. Based on this information, the driver begins their journey to the user's location.

[0296] Step 7:

[0297] After a vehicle assignment is complete, the user rates the driver through the application. The rating information is sent to the server and used as feedback in subsequent matching processes. The input is the user's rating data, and the output is the saved rating as feedback.

[0298] (Application Example 1)

[0299] 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."

[0300] In food delivery services, challenges exist in selecting the optimal delivery professional to match individual customer preferences and in providing quick and efficient pricing. Furthermore, there is a need to improve service quality through real-time demand forecasting and optimal delivery personnel deployment.

[0301] 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.

[0302] In this invention, the server includes means for receiving the user's current location and destination information, means for analyzing the user's past usage history and preference information to select the most suitable delivery person, and means for providing the selected delivery person with information on the optimal travel route and high-demand areas. This enables rapid matching of delivery professionals with the individual user's needs and pricing.

[0303] "User" refers to a person or organization that receives goods or services.

[0304] "Current location" refers to the geographical location where the user is present in real-time.

[0305] "Destination" refers to the geographical location that the user wishes to reach.

[0306] "Usage history information" refers to data related to past transactions or service uses made by the user.

[0307] "Preference information" refers to data related to individual preferences based on the choices and evaluations shown by the user in the past.

[0308] "Delivery staff" refers to the personnel who receive orders and perform the task of delivering goods to the designated destination.

[0309] "Optimal movement route" refers to the route set so that the time and distance for the delivery staff to reach the destination are minimized.

[0310] "High-demand area information" refers to data related to the situation where orders and delivery requests are concentrated in a specific area.

[0311] "Real-time" refers to the state where transmission, processing, and reaction are performed immediately.

[0312] "Fee" refers to the monetary consideration that the user pays for the provided service or goods.

[0313] "Matching" refers to the process of finding and linking the optimal combination between the user and the delivery staff.

[0314] The system that implements this application consists mainly of the user's smart device (terminal) and a central server. When a user wants food delivery, they place an order using their terminal. At this time, the user's current location information, destination information, past usage history, and preference information are transmitted to the server as digital data.

[0315] The server utilizes AI agents to select the optimal delivery person based on historical evaluation data, real-time road conditions, and order demand forecasts. The AI ​​agents employ tools such as TensorFlow and PyTorch. Additionally, a dedicated engine runs to analyze real-time data for calculating delivery fees and estimated delivery times.

[0316] Selected delivery drivers are provided with information on optimal routes and high-demand areas via smart devices. This allows drivers to efficiently deliver goods to their destinations. After delivery is complete, users rate the delivery driver on their device, and this feedback is used in future matching processes.

[0317] For example, if a customer orders a pizza on a weekend evening, the system quickly selects the most suitable pizza delivery person and arranges for the order to be delivered within 30 minutes. Once the order is confirmed, the delivery person receives real-time information on the shortest route and executes the delivery smoothly.

[0318] An example of a prompt message is: "Select the best food delivery driver for the address specified by the user. Calculate the estimated arrival time and delivery fee, taking into account the driver's past ratings and current traffic conditions."

[0319] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0320] Step 1:

[0321] The user places a food delivery order using a terminal. In this step, the user enters their current location, destination, and order details. The input data is collected by the terminal and sent to the server as a digital signal. The output is the transmission of data to the server.

[0322] Step 2:

[0323] The server receives the transmitted data and uses an AI agent to analyze the user's past usage history and preferences. This analysis generates initial data for selecting the most suitable delivery person. The input is the user's past evaluation data, and the output is a list of candidate delivery people.

[0324] Step 3:

[0325] The server analyzes traffic conditions and order demand forecasts in real time, integrates the results with user information, and selects the most suitable delivery person for each delivery. This process uses data analysis algorithms for optimization. The inputs are traffic data and demand data, and the output is the selected delivery person.

[0326] Step 4:

[0327] The server notifies selected delivery personnel of the optimal travel route and information on high-demand areas. This notification enables delivery personnel to make deliveries efficiently. The input is the result of the delivery personnel selection, and the output is the information provided to the delivery personnel.

[0328] Step 5:

[0329] The user confirms the displayed price and estimated arrival time via the terminal and confirms the order. The input is the information displayed by the server, and the output is the user's confirmation.

[0330] Step 6:

[0331] A delivery person delivers the goods from the pickup point to the destination, completing the delivery. After delivery is complete, the user rates the delivery person on their device. This rating data is sent to the server and used in future matching processes. The input is the user's rating, and the output is feedback to the server.

[0332] 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.

[0333] This invention combines emotion recognition technology with a ride-hailing service system designed to meet users' transportation needs. The system utilizes an AI agent to provide an optimal ride-hailing experience that takes into account the user's emotional state.

[0334] System Configuration

[0335] 1. The terminal is a smart device used by the user to send requests and collects data to analyze the user's emotions (e.g., voice tone and input speed) in addition to basic location information. This emotional information is then sent to the server as part of the request data.

[0336] 2. Upon receiving this emotion information, the server activates the emotion engine and evaluates the user's current emotional state. The emotion engine combines natural language processing and speech analysis technologies to recognize states such as stress, joy, and anger.

[0337] 3. The server uses the results obtained from the emotion engine to consider the user's emotional state when the AI ​​agent selects the most suitable driver candidate. For example, if the user is feeling stressed, a driver with particularly good customer service skills will be given priority.

[0338] 4. The optimal pricing offered to users may also be adjusted to take their emotional state into consideration. For example, users experiencing stress may be offered special offers to minimize price fluctuations.

[0339] 5. Drivers' devices receive real-time notifications of tips for providing considerate service to passengers before they board. This allows drivers to respond more appropriately to passengers.

[0340] 6. After the journey is complete, the user provides feedback on the driver and the overall service via the application. This feedback is analyzed by the emotion engine and used to improve the service in the future.

[0341] Specific example

[0342] For example, if a user is in a hurry and requests to be "arrived as quickly as possible," the server recognizes the user's urgency based on their voice tone and the speed of their message. Based on this, the server selects a driver who can arrive immediately, providing a fast service that meets the user's needs.

[0343] This invention's system enables flexible ride-hailing services that are sensitive to the user's feelings, further improving the user experience.

[0344] The following describes the processing flow.

[0345] Step 1:

[0346] The user opens an application on their smart device and enters a ride request. This includes their current location, destination, preferences (e.g., vehicle type and driver rating), as well as sentiment data entered via voice or message.

[0347] Step 2:

[0348] The device prepares the request data and sends location information, usage history, and sentiment data to the server all at once. The sentiment data consists of analysis of voice tone and text input speed.

[0349] Step 3:

[0350] Based on the received data, the server's AI agent analyzes the user's past usage history and preferences. Simultaneously, an emotion engine is activated, analyzing voice and text data to estimate the user's emotional state. Emotions are categorized into types such as stress, relief, and anxiety.

[0351] Step 4:

[0352] Based on the output of the emotion engine, the server's AI agent selects the most appropriate driver. If the user is experiencing stress, the matching process prioritizes drivers with high service ratings.

[0353] Step 5:

[0354] The server calculates the optimal fare based on real-time traffic conditions and supply-demand balance, while also considering the user's emotional state, and presents it to the user in the most reassuring way. This result is transmitted to the terminal in real time and displayed to the user.

[0355] Step 6:

[0356] The driver's terminal receives service delivery hints from the server based on the user's emotional information. The driver uses this information to adjust the service during the ride.

[0357] Step 7:

[0358] After the ride is complete, users provide feedback on the driver and the overall service through the application. This feedback is re-analyzed by an emotion engine and used to improve future matching and service.

[0359] (Example 2)

[0360] 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".

[0361] Traditional ride-hailing service systems have struggled to provide a high-quality customer experience without considering the emotional state of users. In particular, when users' emotional states, such as stress and dissatisfaction, affect the service experience, a more flexible and emotionally resonant service is needed. There is a need for methods to address these challenges and improve user satisfaction.

[0362] 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.

[0363] In this invention, the server includes means for receiving the user's current location and destination location information, means for evaluating the user's emotional state from voice and input data, and means for selecting a driver based on the emotional evaluation and adapting to the service provision. This makes it possible to provide considerate service that responds to the user's emotions.

[0364] "Current location information" refers to data that indicates the real-time location of the device being used by the user.

[0365] "Destination location information" is data that indicates the location of the point the user wants to reach.

[0366] "Usage history information" refers to records of past service usage by users, including date and time, route, and driver information.

[0367] "Preference information" refers to data about individual preferences based on the conditions and characteristics of services that users like, as well as past evaluations.

[0368] A "driver" refers to a person who is responsible for transporting users to their destination when providing a ride-hailing service.

[0369] The "optimal route" is the route chosen by the driver to transport passengers to their destination quickly and efficiently.

[0370] "High-demand local information" refers to information about areas where many users need the service at a specific time and place.

[0371] "Emotional state" refers to the user's psychological and emotional condition and is evaluated by changes in voice tone and input speed.

[0372] "Voice and input data" refers to information about voice and text input acquired by the user's device, from which emotional states can be interpreted.

[0373] "Emotional assessment" is the process of analyzing and understanding users' emotions based on collected data.

[0374] "Matching" refers to the process of selecting and connecting the driver best suited to the user's needs.

[0375] The ride-hailing service system of this invention consists of a user, a terminal, and a server. First, the user requests a ride using a smart device. The user's terminal collects emotional information by analyzing location information, voice tone, and input speed. This collected data is transmitted to the server.

[0376] The server receives this data and uses an emotion engine to evaluate the user's emotional state. The emotion engine uses natural language processing and speech analysis technologies to specifically assess the user's emotions. Based on this evaluation, the AI ​​agent selects a driver. For example, if the user is evaluated as feeling stressed, a driver with excellent customer service skills may be selected. In addition, the driver is notified of virtual service hints, enabling them to provide service that takes the user's emotions into consideration.

[0377] As a concrete example, if a user is in a hurry and requests by voice, "I want to arrive on time," the server will recognize the urgency from the tone of voice and the speed of the message. This will then select the driver who can respond most quickly.

[0378] In this system, the generative AI model enables the delivery of services based on user emotions, providing a flexible and personalized ride-hailing experience.

[0379] Example of a prompt

[0380] "Please tell me what kind of considerations are possible when a user is feeling anxious."

[0381] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0382] Step 1:

[0383] The terminal receives ride-hailing requests from users, including destinations and desired times. In addition, it acquires data to infer the user's emotions, such as the speed of voice and text input and their tone of voice. This entire set of data is then input to the server.

[0384] Step 2:

[0385] The server receives request data and associated emotion data sent from the terminal. The received data is passed to the emotion engine, which analyzes the user's emotional state using natural language processing and speech analysis techniques. This data processing results in the output of emotional states such as stress, joy, and anger.

[0386] Step 3:

[0387] The server uses an AI agent based on the analysis results of the emotion engine to select the appropriate driver. The selection process considers the evaluated emotional state, the driver's current location, estimated arrival time, and past user ratings to output information on the most suitable driver.

[0388] Step 4:

[0389] The server notifies the selected driver's terminal in real time with hints for providing service that take into account the user's emotional state. For example, it might send a message such as, "The user is feeling stressed, so try to respond calmly." This allows the driver to follow the guidance and respond more appropriately.

[0390] Step 5:

[0391] After the journey is complete, the user provides feedback on the driver and the service provided. This feedback is sent to the server and analyzed again by the sentiment engine. The analyzed feedback becomes important data for improving the service in the future.

[0392] (Application Example 2)

[0393] 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."

[0394] Traditional ride-hailing services did not take into account the emotional state of users, resulting in a uniform service delivery. Therefore, there were limitations in providing a satisfactory ride-hailing experience when users were stressed or seeking relaxation. Furthermore, the matching of drivers and fare presentations did not take users' emotions into consideration, potentially leading to decreased customer satisfaction.

[0395] 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.

[0396] In this invention, the server includes means for receiving the user's current emotional state, means for providing the driver and the user with the optimal route and high-demand area information in real time based on the user's emotional state, and means for calculating and presenting a fare adjusted to the user's emotional state. This makes it possible to provide a flexible ride-hailing service that is attentive to the user's emotions.

[0397] "Means for receiving the user's current emotional state" refers to technology that analyzes voice data and input speed obtained from the user to detect and receive their emotional state in real time.

[0398] "Means for receiving the user's current location and destination information" refers to technology for collecting geographical information of the starting point and destination specified by the user and incorporating it into the system.

[0399] "A means of selecting the optimal driver by analyzing the user's past usage history and preference information" refers to a technology that analyzes past usage history and personal preference data to select the driver best suited to the user.

[0400] "Means of providing drivers and users with real-time optimal routes and information on high-demand areas" refers to a function that provides drivers and users with real-time information on optimal routes and areas with high demand, based on current traffic conditions and demand forecast data.

[0401] "A means of calculating and presenting a fee adjusted to the user's emotional state" refers to a technology that, based on the results of an emotional analysis of the user, sets a fee that takes into account stress reduction and the provision of a sense of security, and then presents this to the user.

[0402] "Means for matching selected drivers with users" refers to a method of efficiently providing services by connecting the most suitable drivers and users based on analyzed information.

[0403] The embodiments for carrying out this invention primarily relate to a server, a user's smart device, and an autonomous vehicle. The server receives voice data and input speed from the user's smart device and detects the user's current emotional state in real time using sentiment analysis technology. This involves converting speech to text using the Google Cloud Speech-to-Text API and then performing sentiment analysis using IBM Watson Natural Language Understanding.

[0404] The user's smart device provides current location and destination information and transmits this information to the server. Furthermore, based on past usage history and preference information, the server analyzes and selects the most suitable driver. The server also utilizes AI models to predict demand in real time and provides drivers and users with the most efficient routes and information on high-demand areas.

[0405] Another important element of this program is fare adjustment based on the user's emotional state. This ensures that the most appropriate service is provided to the user. For example, if a family is going on a weekend trip to the beach and the system determines that they are feeling stressed at the start of the trip, it will play relaxing music or select a route with less traffic.

[0406] An example of a prompt to input into the generating AI model would be, "The user is feeling a little stressed at the start of their trip. Based on the emotion recognition results, how would you adjust the environment to help them relax?" This question asks about the quality of service required based on a specific emotional state.

[0407] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0408] Step 1:

[0409] The user's device acquires the user's voice data and input speed. This data is sent to the server as input. The server uses the Google Cloud Speech-to-Text API to convert the voice data into text and prepares it for analyzing the user's emotional state.

[0410] Step 2:

[0411] The server uses IBM Watson Natural Language Understanding to perform sentiment analysis on text obtained from speech. This analysis detects emotions such as stress, joy, and anger. The analysis results are generated as sentiment tags, which serve as input for the next step.

[0412] Step 3:

[0413] The server receives the user's current location and destination information. This information is combined with past usage history and preference data, and a generative AI model is used to select the most suitable driver. The output is a list of potential optimal drivers.

[0414] Step 4:

[0415] The server provides drivers and users with real-time information on optimal routes and high-demand areas. This decision-making process utilizes AI technology to acquire demand forecasting data and evaluate the convenience of each route. The output provides real-time driving routes and related information.

[0416] Step 5:

[0417] The server calculates a fee adjusted for the user based on their emotional state. If the emotional state indicates stress, adjustments such as discounts are applied. This calculates the optimal fee for the user, and this information is presented to the user. The output is the adjusted fee information.

[0418] Step 6:

[0419] The server matches selected drivers with users. This process utilizes previously obtained sentiment tags and historical information, with the driver's response skills being the evaluation criterion. The selection results are output, and a match is confirmed.

[0420] Step 7:

[0421] After the ride-hailing experience ends, users send feedback about the driver and service to the server via their device. The server analyzes this feedback to improve future services and stores it as new data. The input is feedback information, and the output is trend analysis data as a result of the analysis.

[0422] 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.

[0423] 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.

[0424] 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.

[0425] [Third Embodiment]

[0426] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0427] 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.

[0428] 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).

[0429] 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.

[0430] 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.

[0431] 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).

[0432] 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.

[0433] 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.

[0434] 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.

[0435] 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.

[0436] 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.

[0437] 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".

[0438] This invention relates to a ride-hailing service system based on the user's transportation needs. The system uses an AI agent to select the most suitable driver, enabling rapid matching and fare quotes. The following describes specific embodiments of the system.

[0439] System Configuration

[0440] 1. The terminal functions as a smart device and provides a means for the user to input their current location, destination, and preferences (such as vehicle type and driver rating). This information is transmitted as a digital signal to a server within the system.

[0441] 2. The server is a central system that processes the received data. The server is equipped with an analysis module that includes an AI agent, which selects the optimal driver based on the user's history and preferences.

[0442] 3. The server then uses the information entered by the user and the results of the AI ​​analysis to activate the pricing engine. This engine considers real-time supply and demand data to calculate an appropriate price for the user.

[0443] 4. The terminals used by drivers work in conjunction with the server to provide drivers with information on optimal routes and high-demand areas. This information is based on demand forecasts generated by an AI agent.

[0444] 5. Once the user approves the provided ride conditions (fare, driver, estimated arrival time, etc.), the server sends the pickup location information to the driver, and the actual dispatch begins.

[0445] 6. After the dispatch is complete, the user rates the driver through the application. This rate is stored on the server and used as feedback for future matching algorithms.

[0446] Specific example

[0447] For example, if a user enters "I would like a ride from my office to the airport," the terminal sends that geographical information to the server. The server's AI agent selects the most suitable driver for that user based on past history and rating data. Next, the fare is calculated based on real-time traffic conditions, and the terminal displays the result to the user. After confirmation via the mobile app, the finally selected driver heads to the pickup location.

[0448] This invention's system enables efficient vehicle dispatching and provides a highly satisfying service to both users and drivers.

[0449] The following describes the processing flow.

[0450] Step 1:

[0451] The user accesses an application on their smart device and enters their current location, destination, and preferred criteria (such as vehicle type and driver rating). This generates request data.

[0452] Step 2:

[0453] The device sends this request data to the server. This data is transferred either as text or as structured data via an API.

[0454] Step 3:

[0455] The server analyzes the received request data and searches the database, taking into account the user's past usage history and preferences. This generates a list of optimal driver candidates.

[0456] Step 4:

[0457] An AI agent on the server uses real-time traffic conditions and geographical information to predict demand in each area. Based on these results, it optimizes driver deployment and selects the most suitable driver.

[0458] Step 5:

[0459] The server provides the selected driver with information on optimal routes and high-demand areas, and notifies the driver. The driver's terminal receives this information and begins moving towards the pickup location.

[0460] Step 6:

[0461] The server uses real-time demand data to calculate the appropriate price to present to the user. This pricing information is sent to the terminal and presented to the user in advance.

[0462] Step 7:

[0463] Once the user accepts the presented terms, the dispatch officially begins. The driver heads to the customer's pickup location and starts moving.

[0464] Step 8:

[0465] After the transfer is complete, the user evaluates the driver through the application. This evaluation is stored on the server and used for future driver matching.

[0466] (Example 1)

[0467] 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."

[0468] In today's transportation landscape, rapid and accurate vehicle allocation is essential. However, conventional systems often fail to respond quickly to fluctuations in demand, leading to decreased customer satisfaction. Furthermore, insufficient driver matching quality has resulted in reduced travel efficiency. Moreover, the lack of driver feedback being utilized in the matching process means that opportunities for improvement are lost.

[0469] 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.

[0470] In this invention, the server includes a device for receiving the user's geographical location information and destination information, a device for selecting the optimal driver by analyzing the user's past usage records and preference information, and a device for calculating and displaying the optimal fare to the user, taking time information into consideration. This enables the provision of an optimal ride-hailing service that responds to demand in real time and high-quality matching with drivers.

[0471] "Geographic location information" refers to data that indicates the specific physical location where the user is currently located.

[0472] "Destination information" refers to data that indicates the location of the destination the user is aiming for.

[0473] "Past usage records" refer to information about the history of services that a user has used in the past.

[0474] "Preference information" refers to information that indicates the characteristics of drivers that users prefer and their personal preferences regarding vehicles.

[0475] "Driver" refers to an individual or person who provides transportation services in response to the user's request.

[0476] "Data processing technology" refers to the technical means of processing large amounts of information quickly and efficiently.

[0477] A "terminal" is an electronic device used by users to input information or receive results.

[0478] The "analysis process" is the procedure for selecting the optimal driver based on the received data.

[0479] "Feedback" refers to data that shows the evaluations and opinions that users provide regarding the quality of a service.

[0480] This invention is a ride-hailing service system that enables users to travel smoothly. Users use a device such as a smartphone or tablet to launch an application. The application provides an interface for inputting geographical location information and destination information. This allows users to input their departure point, destination, and preferred vehicle and driver conditions.

[0481] The terminal transmits the input information as a digital signal to a server within the system. The server receives this information using high-performance data processing technology and uses an AI-powered analysis module to select the most suitable driver based on the user's past usage history and preferences. This process aims to enable efficient driver selection and provide users with high-quality ride-hailing services.

[0482] In terms of specific hardware, smart devices function as terminals, and a computing system including a central processing unit operates as a server. The software running on the server includes an AI agent and a fare calculation engine. The AI ​​agent uses a generative AI model to optimize the matching of users and drivers, and the fare calculation engine considers real-time supply and demand conditions to present users with appropriate fares.

[0483] As a concrete example, if a user enters "I would like a ride from my office to the airport," the terminal sends this information to the server. The server uses an AI agent to select the most suitable driver for the user and calculates the fare based on real-time traffic information. The result is then presented to the user through the terminal. Once the most suitable driver is selected and approved, the ride-hailing service begins. An example of a prompt to be entered into the generating AI model would be: "Please arrange the most suitable ride from my current location (office) to the airport. I would like a sedan, and please select a driver with a high past rating."

[0484] By implementing this invention, it is possible to provide efficient and highly satisfying mobility services for both users and drivers.

[0485] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0486] Step 1:

[0487] Users access the application using a smart device and input geographical location information, destination information, and desired vehicle and driver conditions. This input data constitutes the user's travel needs. The device converts this information into digital signals and prepares them for transmission to a server within the system.

[0488] Step 2:

[0489] The terminal transmits the input information, converted as a digital signal, to a server within the system. This input consists of user requests such as geographical location information, destination information, and desired conditions. The server receives this information as output, preparing it for the next processing stage.

[0490] Step 3:

[0491] The server compares the received user information with past usage records and preference information stored in the database, and performs analysis using a generating AI model. This process selects a driver based on past evaluations and preferences. The input is user information and historical data, and the output is the result of selecting the optimal driver.

[0492] Step 4:

[0493] The server activates the fare calculation engine and calculates fares taking into account real-time traffic conditions and the balance of supply and demand. Inputs include traffic data and supply and demand information, and the output is the appropriate fare to be presented to the user. This calculation is adapted to current market conditions.

[0494] Step 5:

[0495] The server transmits the selected driver information and calculated fare to the terminal and presents it to the user. The input is the driver and fare information, and the output is displayed on the user's screen. The user reviews the presented conditions and chooses to accept or reject them.

[0496] Step 6:

[0497] Once the user approves the presented conditions, the server sends pickup location information to the driver's terminal based on the approved information. The input is the approved matching information, and the output is a work instruction for the driver. Based on this information, the driver begins their journey to the user's location.

[0498] Step 7:

[0499] After a vehicle assignment is complete, the user rates the driver through the application. The rating information is sent to the server and used as feedback in subsequent matching processes. The input is the user's rating data, and the output is the saved rating as feedback.

[0500] (Application Example 1)

[0501] 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."

[0502] In food delivery services, challenges exist in selecting the optimal delivery professional to match individual customer preferences and in providing quick and efficient pricing. Furthermore, there is a need to improve service quality through real-time demand forecasting and optimal delivery personnel deployment.

[0503] 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.

[0504] In this invention, the server includes means for receiving the user's current location and destination information, means for analyzing the user's past usage history and preference information to select the most suitable delivery person, and means for providing the selected delivery person with information on the optimal travel route and high-demand areas. This enables rapid matching of delivery professionals with the individual user's needs and pricing.

[0505] "User" refers to an individual or group that receives a product or service.

[0506] "Current location" refers to the geographical location where the user is located in real time.

[0507] "Destination" refers to the geographical location that the user wishes to reach.

[0508] "Usage history information" refers to data related to transactions and service usage that a user has performed in the past.

[0509] "Preference information" refers to data about individual preferences based on choices and evaluations that users have made in the past.

[0510] A "delivery person" refers to a person who receives an order and delivers the goods to the designated destination.

[0511] An "optimal travel route" refers to a route that is set up to minimize the time and distance required for a delivery person to reach their destination.

[0512] "High-demand area information" refers to data on situations where orders and delivery requests are concentrated in specific regions.

[0513] "Real-time" refers to a situation where transmission, processing, or response occurs instantly.

[0514] "Fees" refer to the monetary compensation that a user pays for a service or product provided.

[0515] "Matching" refers to the process of finding and connecting the optimal combination of users and delivery personnel.

[0516] The system that implements this application consists mainly of the user's smart device (terminal) and a central server. When a user wants food delivery, they place an order using their terminal. At this time, the user's current location information, destination information, past usage history, and preference information are transmitted to the server as digital data.

[0517] The server utilizes AI agents to select the optimal delivery person based on historical evaluation data, real-time road conditions, and order demand forecasts. The AI ​​agents employ tools such as TensorFlow and PyTorch. Additionally, a dedicated engine runs to analyze real-time data for calculating delivery fees and estimated delivery times.

[0518] Selected delivery drivers are provided with information on optimal routes and high-demand areas via smart devices. This allows drivers to efficiently deliver goods to their destinations. After delivery is complete, users rate the delivery driver on their device, and this feedback is used in future matching processes.

[0519] For example, if a customer orders a pizza on a weekend evening, the system quickly selects the most suitable pizza delivery person and arranges for the order to be delivered within 30 minutes. Once the order is confirmed, the delivery person receives real-time information on the shortest route and executes the delivery smoothly.

[0520] An example of a prompt message is: "Select the best food delivery driver for the address specified by the user. Calculate the estimated arrival time and delivery fee, taking into account the driver's past ratings and current traffic conditions."

[0521] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0522] Step 1:

[0523] The user places a food delivery order using a terminal. In this step, the user enters their current location, destination, and order details. The input data is collected by the terminal and sent to the server as a digital signal. The output is the transmission of data to the server.

[0524] Step 2:

[0525] The server receives the transmitted data and uses an AI agent to analyze the user's past usage history and preferences. This analysis generates initial data for selecting the most suitable delivery person. The input is the user's past evaluation data, and the output is a list of candidate delivery people.

[0526] Step 3:

[0527] The server analyzes traffic conditions and order demand forecasts in real time, integrates the results with user information, and selects the most suitable delivery person for each delivery. This process uses data analysis algorithms for optimization. The inputs are traffic data and demand data, and the output is the selected delivery person.

[0528] Step 4:

[0529] The server notifies selected delivery personnel of the optimal travel route and information on high-demand areas. This notification enables delivery personnel to make deliveries efficiently. The input is the result of the delivery personnel selection, and the output is the information provided to the delivery personnel.

[0530] Step 5:

[0531] The user confirms the displayed price and estimated arrival time via the terminal and confirms the order. The input is the information displayed by the server, and the output is the user's confirmation.

[0532] Step 6:

[0533] A delivery person delivers the goods from the pickup point to the destination, completing the delivery. After delivery is complete, the user rates the delivery person on their device. This rating data is sent to the server and used in future matching processes. The input is the user's rating, and the output is feedback to the server.

[0534] 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.

[0535] This invention combines emotion recognition technology with a ride-hailing service system designed to meet users' transportation needs. The system utilizes an AI agent to provide an optimal ride-hailing experience that takes into account the user's emotional state.

[0536] System Configuration

[0537] 1. The terminal is a smart device used by the user to send requests and collects data to analyze the user's emotions (e.g., voice tone and input speed) in addition to basic location information. This emotional information is then sent to the server as part of the request data.

[0538] 2. Upon receiving this emotion information, the server activates the emotion engine and evaluates the user's current emotional state. The emotion engine combines natural language processing and speech analysis technologies to recognize states such as stress, joy, and anger.

[0539] 3. The server uses the results obtained from the emotion engine to consider the user's emotional state when the AI ​​agent selects the most suitable driver candidate. For example, if the user is feeling stressed, a driver with particularly good customer service skills will be given priority.

[0540] 4. The optimal pricing offered to users may also be adjusted to take their emotional state into consideration. For example, users experiencing stress may be offered special offers to minimize price fluctuations.

[0541] 5. Drivers' devices receive real-time notifications of tips for providing considerate service to passengers before they board. This allows drivers to respond more appropriately to passengers.

[0542] 6. After the journey is complete, the user provides feedback on the driver and the overall service via the application. This feedback is analyzed by the emotion engine and used to improve the service in the future.

[0543] Specific example

[0544] For example, if a user is in a hurry and requests to be "arrived as quickly as possible," the server recognizes the user's urgency based on their voice tone and the speed of their message. Based on this, the server selects a driver who can arrive immediately, providing a fast service that meets the user's needs.

[0545] This invention's system enables flexible ride-hailing services that are sensitive to the user's feelings, further improving the user experience.

[0546] The following describes the processing flow.

[0547] Step 1:

[0548] The user opens an application on their smart device and enters a ride request. This includes their current location, destination, preferences (e.g., vehicle type and driver rating), as well as sentiment data entered via voice or message.

[0549] Step 2:

[0550] The device prepares the request data and sends location information, usage history, and sentiment data to the server all at once. The sentiment data consists of analysis of voice tone and text input speed.

[0551] Step 3:

[0552] Based on the received data, the server's AI agent analyzes the user's past usage history and preferences. Simultaneously, an emotion engine is activated, analyzing voice and text data to estimate the user's emotional state. Emotions are categorized into types such as stress, relief, and anxiety.

[0553] Step 4:

[0554] Based on the output of the emotion engine, the server's AI agent selects the most appropriate driver. If the user is experiencing stress, the matching process prioritizes drivers with high service ratings.

[0555] Step 5:

[0556] The server calculates the optimal fare based on real-time traffic conditions and supply-demand balance, while also considering the user's emotional state, and presents it to the user in the most reassuring way. This result is transmitted to the terminal in real time and displayed to the user.

[0557] Step 6:

[0558] The driver's terminal receives service delivery hints from the server based on the user's emotional information. The driver uses this information to adjust the service during the ride.

[0559] Step 7:

[0560] After the ride is complete, users provide feedback on the driver and the overall service through the application. This feedback is re-analyzed by an emotion engine and used to improve future matching and service.

[0561] (Example 2)

[0562] 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."

[0563] Traditional ride-hailing service systems have struggled to provide a high-quality customer experience without considering the emotional state of users. In particular, when users' emotional states, such as stress and dissatisfaction, affect the service experience, a more flexible and emotionally resonant service is needed. There is a need for methods to address these challenges and improve user satisfaction.

[0564] 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.

[0565] In this invention, the server includes means for receiving the user's current location and destination location information, means for evaluating the user's emotional state from voice and input data, and means for selecting a driver based on the emotional evaluation and adapting to the service provision. This makes it possible to provide considerate service that responds to the user's emotions.

[0566] "Current location information" refers to data that indicates the real-time location of the device being used by the user.

[0567] "Destination location information" is data that indicates the location of the point the user wants to reach.

[0568] "Usage history information" refers to records of past service usage by users, including date and time, route, and driver information.

[0569] "Preference information" refers to data about individual preferences based on the conditions and characteristics of services that users like, as well as past evaluations.

[0570] A "driver" refers to a person who is responsible for transporting users to their destination when providing a ride-hailing service.

[0571] The "optimal route" is the route chosen by the driver to transport passengers to their destination quickly and efficiently.

[0572] "High-demand local information" refers to information about areas where many users need the service at a specific time and place.

[0573] "Emotional state" refers to the user's psychological and emotional condition and is evaluated by changes in voice tone and input speed.

[0574] "Voice and input data" refers to information about voice and text input acquired by the user's device, from which emotional states can be interpreted.

[0575] "Emotional assessment" is the process of analyzing and understanding users' emotions based on collected data.

[0576] "Matching" refers to the process of selecting and connecting the driver best suited to the user's needs.

[0577] The ride-hailing service system of this invention consists of a user, a terminal, and a server. First, the user requests a ride using a smart device. The user's terminal collects emotional information by analyzing location information, voice tone, and input speed. This collected data is transmitted to the server.

[0578] The server receives this data and uses an emotion engine to evaluate the user's emotional state. The emotion engine uses natural language processing and speech analysis technologies to specifically assess the user's emotions. Based on this evaluation, the AI ​​agent selects a driver. For example, if the user is evaluated as feeling stressed, a driver with excellent customer service skills may be selected. In addition, the driver is notified of virtual service hints, enabling them to provide service that takes the user's emotions into consideration.

[0579] As a concrete example, if a user is in a hurry and requests by voice, "I want to arrive on time," the server will recognize the urgency from the tone of voice and the speed of the message. This will then select the driver who can respond most quickly.

[0580] In this system, the generative AI model enables the delivery of services based on user emotions, providing a flexible and personalized ride-hailing experience.

[0581] Example of a prompt

[0582] "Please tell me what kind of considerations are possible when a user is feeling anxious."

[0583] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0584] Step 1:

[0585] The terminal receives ride-hailing requests from users, including destinations and desired times. In addition, it acquires data to infer the user's emotions, such as the speed of voice and text input and their tone of voice. This entire set of data is then input to the server.

[0586] Step 2:

[0587] The server receives request data and associated emotion data sent from the terminal. The received data is passed to the emotion engine, which analyzes the user's emotional state using natural language processing and speech analysis techniques. This data processing results in the output of emotional states such as stress, joy, and anger.

[0588] Step 3:

[0589] The server uses an AI agent based on the analysis results of the emotion engine to select the appropriate driver. The selection process considers the evaluated emotional state, the driver's current location, estimated arrival time, and past user ratings to output information on the most suitable driver.

[0590] Step 4:

[0591] The server notifies the selected driver's terminal in real time with hints for providing service that take into account the user's emotional state. For example, it might send a message such as, "The user is feeling stressed, so try to respond calmly." This allows the driver to follow the guidance and respond more appropriately.

[0592] Step 5:

[0593] After the journey is complete, the user provides feedback on the driver and the service provided. This feedback is sent to the server and analyzed again by the sentiment engine. The analyzed feedback becomes important data for improving the service in the future.

[0594] (Application Example 2)

[0595] 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."

[0596] Traditional ride-hailing services did not take into account the emotional state of users, resulting in a uniform service delivery. Therefore, there were limitations in providing a satisfactory ride-hailing experience when users were stressed or seeking relaxation. Furthermore, the matching of drivers and fare presentations did not take users' emotions into consideration, potentially leading to decreased customer satisfaction.

[0597] 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.

[0598] In this invention, the server includes means for receiving the user's current emotional state, means for providing the driver and the user with the optimal route and high-demand area information in real time based on the user's emotional state, and means for calculating and presenting a fare adjusted to the user's emotional state. This makes it possible to provide a flexible ride-hailing service that is attentive to the user's emotions.

[0599] "Means for receiving the user's current emotional state" refers to technology that analyzes voice data and input speed obtained from the user to detect and receive their emotional state in real time.

[0600] "Means for receiving the user's current location and destination information" refers to technology for collecting geographical information of the starting point and destination specified by the user and incorporating it into the system.

[0601] "A means of selecting the optimal driver by analyzing the user's past usage history and preference information" refers to a technology that analyzes past usage history and personal preference data to select the driver best suited to the user.

[0602] "Means of providing drivers and users with real-time optimal routes and information on high-demand areas" refers to a function that provides drivers and users with real-time information on optimal routes and areas with high demand, based on current traffic conditions and demand forecast data.

[0603] "A means of calculating and presenting a fee adjusted to the user's emotional state" refers to a technology that, based on the results of an emotional analysis of the user, sets a fee that takes into account stress reduction and the provision of a sense of security, and then presents this to the user.

[0604] "Means for matching selected drivers with users" refers to a method of efficiently providing services by connecting the most suitable drivers and users based on analyzed information.

[0605] The embodiments for carrying out this invention primarily relate to a server, a user's smart device, and an autonomous vehicle. The server receives voice data and input speed from the user's smart device and detects the user's current emotional state in real time using sentiment analysis technology. This involves converting speech to text using the Google Cloud Speech-to-Text API and then performing sentiment analysis using IBM Watson Natural Language Understanding.

[0606] The user's smart device provides current location and destination information and transmits this information to the server. Furthermore, based on past usage history and preference information, the server analyzes and selects the most suitable driver. The server also utilizes AI models to predict demand in real time and provides drivers and users with the most efficient routes and information on high-demand areas.

[0607] Another important element of this program is fare adjustment based on the user's emotional state. This ensures that the most appropriate service is provided to the user. For example, if a family is going on a weekend trip to the beach and the system determines that they are feeling stressed at the start of the trip, it will play relaxing music or select a route with less traffic.

[0608] An example of a prompt to input into the generating AI model would be, "The user is feeling a little stressed at the start of their trip. Based on the emotion recognition results, how would you adjust the environment to help them relax?" This question asks about the quality of service required based on a specific emotional state.

[0609] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0610] Step 1:

[0611] The user's device acquires the user's voice data and input speed. This data is sent to the server as input. The server uses the Google Cloud Speech-to-Text API to convert the voice data into text and prepares it for analyzing the user's emotional state.

[0612] Step 2:

[0613] The server uses IBM Watson Natural Language Understanding to perform sentiment analysis on text obtained from speech. This analysis detects emotions such as stress, joy, and anger. The analysis results are generated as sentiment tags, which serve as input for the next step.

[0614] Step 3:

[0615] The server receives the user's current location and destination information. This information is combined with past usage history and preference data, and a generative AI model is used to select the most suitable driver. The output is a list of potential optimal drivers.

[0616] Step 4:

[0617] The server provides drivers and users with real-time information on optimal routes and high-demand areas. This decision-making process utilizes AI technology to acquire demand forecasting data and evaluate the convenience of each route. The output provides real-time driving routes and related information.

[0618] Step 5:

[0619] The server calculates a fee adjusted for the user based on their emotional state. If the emotional state indicates stress, adjustments such as discounts are applied. This calculates the optimal fee for the user, and this information is presented to the user. The output is the adjusted fee information.

[0620] Step 6:

[0621] The server matches selected drivers with users. This process utilizes previously obtained sentiment tags and historical information, with the driver's response skills being the evaluation criterion. The selection results are output, and a match is confirmed.

[0622] Step 7:

[0623] After the ride-hailing experience ends, users send feedback about the driver and service to the server via their device. The server analyzes this feedback to improve future services and stores it as new data. The input is feedback information, and the output is trend analysis data as a result of the analysis.

[0624] 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.

[0625] 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.

[0626] 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.

[0627] [Fourth Embodiment]

[0628] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0629] 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.

[0630] 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).

[0631] 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.

[0632] 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.

[0633] 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).

[0634] 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.

[0635] 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.

[0636] 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.

[0637] 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.

[0638] 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.

[0639] 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.

[0640] 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".

[0641] This invention relates to a ride-hailing service system based on the user's transportation needs. The system uses an AI agent to select the most suitable driver, enabling rapid matching and fare quotes. The following describes specific embodiments of the system.

[0642] System Configuration

[0643] 1. The terminal functions as a smart device and provides a means for the user to input their current location, destination, and preferences (such as vehicle type and driver rating). This information is transmitted as a digital signal to a server within the system.

[0644] 2. The server is a central system that processes the received data. The server is equipped with an analysis module that includes an AI agent, which selects the optimal driver based on the user's history and preferences.

[0645] 3. The server then uses the information entered by the user and the results of the AI ​​analysis to activate the pricing engine. This engine considers real-time supply and demand data to calculate an appropriate price for the user.

[0646] 4. The terminals used by drivers work in conjunction with the server to provide drivers with information on optimal routes and high-demand areas. This information is based on demand forecasts generated by an AI agent.

[0647] 5. Once the user approves the provided ride conditions (fare, driver, estimated arrival time, etc.), the server sends the pickup location information to the driver, and the actual dispatch begins.

[0648] 6. After the dispatch is complete, the user rates the driver through the application. This rate is stored on the server and used as feedback for future matching algorithms.

[0649] Specific example

[0650] For example, if a user enters "I would like a ride from my office to the airport," the terminal sends that geographical information to the server. The server's AI agent selects the most suitable driver for that user based on past history and rating data. Next, the fare is calculated based on real-time traffic conditions, and the terminal displays the result to the user. After confirmation via the mobile app, the finally selected driver heads to the pickup location.

[0651] This invention's system enables efficient vehicle dispatching and provides a highly satisfying service to both users and drivers.

[0652] The following describes the processing flow.

[0653] Step 1:

[0654] The user accesses an application on their smart device and enters their current location, destination, and preferred criteria (such as vehicle type and driver rating). This generates request data.

[0655] Step 2:

[0656] The device sends this request data to the server. This data is transferred either as text or as structured data via an API.

[0657] Step 3:

[0658] The server analyzes the received request data and searches the database, taking into account the user's past usage history and preferences. This generates a list of optimal driver candidates.

[0659] Step 4:

[0660] An AI agent on the server uses real-time traffic conditions and geographical information to predict demand in each area. Based on these results, it optimizes driver deployment and selects the most suitable driver.

[0661] Step 5:

[0662] The server provides the selected driver with information on optimal routes and high-demand areas, and notifies the driver. The driver's terminal receives this information and begins moving towards the pickup location.

[0663] Step 6:

[0664] The server uses real-time demand data to calculate the appropriate price to present to the user. This pricing information is sent to the terminal and presented to the user in advance.

[0665] Step 7:

[0666] Once the user accepts the presented terms, the dispatch officially begins. The driver heads to the customer's pickup location and starts moving.

[0667] Step 8:

[0668] After the transfer is complete, the user evaluates the driver through the application. This evaluation is stored on the server and used for future driver matching.

[0669] (Example 1)

[0670] 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".

[0671] In today's transportation landscape, rapid and accurate vehicle allocation is essential. However, conventional systems often fail to respond quickly to fluctuations in demand, leading to decreased customer satisfaction. Furthermore, insufficient driver matching quality has resulted in reduced travel efficiency. Moreover, the lack of driver feedback being utilized in the matching process means that opportunities for improvement are lost.

[0672] 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.

[0673] In this invention, the server includes a device for receiving the user's geographical location information and destination information, a device for selecting the optimal driver by analyzing the user's past usage records and preference information, and a device for calculating and displaying the optimal fare to the user, taking time information into consideration. This enables the provision of an optimal ride-hailing service that responds to demand in real time and high-quality matching with drivers.

[0674] "Geographic location information" refers to data that indicates the specific physical location where the user is currently located.

[0675] "Destination information" refers to data that indicates the location of the destination the user is aiming for.

[0676] "Past usage records" refer to information about the history of services that a user has used in the past.

[0677] "Preference information" refers to information that indicates the characteristics of drivers that users prefer and their personal preferences regarding vehicles.

[0678] "Driver" refers to an individual or person who provides transportation services in response to the user's request.

[0679] "Data processing technology" refers to the technical means of processing large amounts of information quickly and efficiently.

[0680] A "terminal" is an electronic device used by users to input information or receive results.

[0681] The "analysis process" is the procedure for selecting the optimal driver based on the received data.

[0682] "Feedback" refers to data that shows the evaluations and opinions that users provide regarding the quality of a service.

[0683] This invention is a ride-hailing service system that enables users to travel smoothly. Users use a device such as a smartphone or tablet to launch an application. The application provides an interface for inputting geographical location information and destination information. This allows users to input their departure point, destination, and preferred vehicle and driver conditions.

[0684] The terminal transmits the input information as a digital signal to a server within the system. The server receives this information using high-performance data processing technology and uses an AI-powered analysis module to select the most suitable driver based on the user's past usage history and preferences. This process aims to enable efficient driver selection and provide users with high-quality ride-hailing services.

[0685] In terms of specific hardware, smart devices function as terminals, and a computing system including a central processing unit operates as a server. The software running on the server includes an AI agent and a fare calculation engine. The AI ​​agent uses a generative AI model to optimize the matching of users and drivers, and the fare calculation engine considers real-time supply and demand conditions to present users with appropriate fares.

[0686] As a concrete example, if a user enters "I would like a ride from my office to the airport," the terminal sends this information to the server. The server uses an AI agent to select the most suitable driver for the user and calculates the fare based on real-time traffic information. The result is then presented to the user through the terminal. Once the most suitable driver is selected and approved, the ride-hailing service begins. An example of a prompt to be entered into the generating AI model would be: "Please arrange the most suitable ride from my current location (office) to the airport. I would like a sedan, and please select a driver with a high past rating."

[0687] By implementing this invention, it is possible to provide efficient and highly satisfying mobility services for both users and drivers.

[0688] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0689] Step 1:

[0690] Users access the application using a smart device and input geographical location information, destination information, and desired vehicle and driver conditions. This input data constitutes the user's travel needs. The device converts this information into digital signals and prepares them for transmission to a server within the system.

[0691] Step 2:

[0692] The terminal transmits the input information, converted as a digital signal, to a server within the system. This input consists of user requests such as geographical location information, destination information, and desired conditions. The server receives this information as output, preparing it for the next processing stage.

[0693] Step 3:

[0694] The server compares the received user information with past usage records and preference information stored in the database, and performs analysis using a generating AI model. This process selects a driver based on past evaluations and preferences. The input is user information and historical data, and the output is the result of selecting the optimal driver.

[0695] Step 4:

[0696] The server activates the fare calculation engine and calculates fares taking into account real-time traffic conditions and the balance of supply and demand. Inputs include traffic data and supply and demand information, and the output is the appropriate fare to be presented to the user. This calculation is adapted to current market conditions.

[0697] Step 5:

[0698] The server transmits the selected driver information and calculated fare to the terminal and presents it to the user. The input is the driver and fare information, and the output is displayed on the user's screen. The user reviews the presented conditions and chooses to accept or reject them.

[0699] Step 6:

[0700] Once the user approves the presented conditions, the server sends pickup location information to the driver's terminal based on the approved information. The input is the approved matching information, and the output is a work instruction for the driver. Based on this information, the driver begins their journey to the user's location.

[0701] Step 7:

[0702] After a vehicle assignment is complete, the user rates the driver through the application. The rating information is sent to the server and used as feedback in subsequent matching processes. The input is the user's rating data, and the output is the saved rating as feedback.

[0703] (Application Example 1)

[0704] 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".

[0705] In food delivery services, challenges exist in selecting the optimal delivery professional to match individual customer preferences and in providing quick and efficient pricing. Furthermore, there is a need to improve service quality through real-time demand forecasting and optimal delivery personnel deployment.

[0706] 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.

[0707] In this invention, the server includes means for receiving the user's current location and destination information, means for analyzing the user's past usage history and preference information to select the most suitable delivery person, and means for providing the selected delivery person with information on the optimal travel route and high-demand areas. This enables rapid matching of delivery professionals with the individual user's needs and pricing.

[0708] "User" refers to an individual or group that receives a product or service.

[0709] "Current location" refers to the geographical location where the user is located in real time.

[0710] "Destination" refers to the geographical location that the user wishes to reach.

[0711] "Usage history information" refers to data related to transactions and service usage that a user has performed in the past.

[0712] "Preference information" refers to data about individual preferences based on choices and evaluations that users have made in the past.

[0713] A "delivery person" refers to a person who receives an order and delivers the goods to the designated destination.

[0714] An "optimal travel route" refers to a route that is set up to minimize the time and distance required for a delivery person to reach their destination.

[0715] "High-demand area information" refers to data on situations where orders and delivery requests are concentrated in specific regions.

[0716] "Real-time" refers to a situation where transmission, processing, or response occurs instantly.

[0717] "Fees" refer to the monetary compensation that a user pays for a service or product provided.

[0718] "Matching" refers to the process of finding and connecting the optimal combination of users and delivery personnel.

[0719] The system that implements this application consists mainly of the user's smart device (terminal) and a central server. When a user wants food delivery, they place an order using their terminal. At this time, the user's current location information, destination information, past usage history, and preference information are transmitted to the server as digital data.

[0720] The server utilizes AI agents to select the optimal delivery person based on historical evaluation data, real-time road conditions, and order demand forecasts. The AI ​​agents employ tools such as TensorFlow and PyTorch. Additionally, a dedicated engine runs to analyze real-time data for calculating delivery fees and estimated delivery times.

[0721] Selected delivery drivers are provided with information on optimal routes and high-demand areas via smart devices. This allows drivers to efficiently deliver goods to their destinations. After delivery is complete, users rate the delivery driver on their device, and this feedback is used in future matching processes.

[0722] For example, if a customer orders a pizza on a weekend evening, the system quickly selects the most suitable pizza delivery person and arranges for the order to be delivered within 30 minutes. Once the order is confirmed, the delivery person receives real-time information on the shortest route and executes the delivery smoothly.

[0723] An example of a prompt message is: "Select the best food delivery driver for the address specified by the user. Calculate the estimated arrival time and delivery fee, taking into account the driver's past ratings and current traffic conditions."

[0724] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0725] Step 1:

[0726] The user places a food delivery order using a terminal. In this step, the user enters their current location, destination, and order details. The input data is collected by the terminal and sent to the server as a digital signal. The output is the transmission of data to the server.

[0727] Step 2:

[0728] The server receives the transmitted data and uses an AI agent to analyze the user's past usage history and preferences. This analysis generates initial data for selecting the most suitable delivery person. The input is the user's past evaluation data, and the output is a list of candidate delivery people.

[0729] Step 3:

[0730] The server analyzes traffic conditions and order demand forecasts in real time, integrates the results with user information, and selects the most suitable delivery person for each delivery. This process uses data analysis algorithms for optimization. The inputs are traffic data and demand data, and the output is the selected delivery person.

[0731] Step 4:

[0732] The server notifies selected delivery personnel of the optimal travel route and information on high-demand areas. This notification enables delivery personnel to make deliveries efficiently. The input is the result of the delivery personnel selection, and the output is the information provided to the delivery personnel.

[0733] Step 5:

[0734] The user confirms the displayed price and estimated arrival time via the terminal and confirms the order. The input is the information displayed by the server, and the output is the user's confirmation.

[0735] Step 6:

[0736] A delivery person delivers the goods from the pickup point to the destination, completing the delivery. After delivery is complete, the user rates the delivery person on their device. This rating data is sent to the server and used in future matching processes. The input is the user's rating, and the output is feedback to the server.

[0737] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0738] This invention combines emotion recognition technology with a ride-hailing service system designed to meet users' transportation needs. The system utilizes an AI agent to provide an optimal ride-hailing experience that takes into account the user's emotional state.

[0739] System Configuration

[0740] 1. The terminal is a smart device used by the user to send requests and collects data to analyze the user's emotions (e.g., voice tone and input speed) in addition to basic location information. This emotional information is then sent to the server as part of the request data.

[0741] 2. Upon receiving this emotion information, the server activates the emotion engine and evaluates the user's current emotional state. The emotion engine combines natural language processing and speech analysis technologies to recognize states such as stress, joy, and anger.

[0742] 3. The server uses the results obtained from the emotion engine to consider the user's emotional state when the AI ​​agent selects the most suitable driver candidate. For example, if the user is feeling stressed, a driver with particularly good customer service skills will be given priority.

[0743] 4. The optimal pricing offered to users may also be adjusted to take their emotional state into consideration. For example, users experiencing stress may be offered special offers to minimize price fluctuations.

[0744] 5. Drivers' devices receive real-time notifications of tips for providing considerate service to passengers before they board. This allows drivers to respond more appropriately to passengers.

[0745] 6. After the journey is complete, the user provides feedback on the driver and the overall service via the application. This feedback is analyzed by the emotion engine and used to improve the service in the future.

[0746] Specific example

[0747] For example, if a user is in a hurry and requests to be "arrived as quickly as possible," the server recognizes the user's urgency based on their voice tone and the speed of their message. Based on this, the server selects a driver who can arrive immediately, providing a fast service that meets the user's needs.

[0748] This invention's system enables flexible ride-hailing services that are sensitive to the user's feelings, further improving the user experience.

[0749] The following describes the processing flow.

[0750] Step 1:

[0751] The user opens an application on their smart device and enters a ride request. This includes their current location, destination, preferences (e.g., vehicle type and driver rating), as well as sentiment data entered via voice or message.

[0752] Step 2:

[0753] The device prepares the request data and sends location information, usage history, and sentiment data to the server all at once. The sentiment data consists of analysis of voice tone and text input speed.

[0754] Step 3:

[0755] Based on the received data, the server's AI agent analyzes the user's past usage history and preferences. Simultaneously, an emotion engine is activated, analyzing voice and text data to estimate the user's emotional state. Emotions are categorized into types such as stress, relief, and anxiety.

[0756] Step 4:

[0757] Based on the output of the emotion engine, the server's AI agent selects the most appropriate driver. If the user is experiencing stress, the matching process prioritizes drivers with high service ratings.

[0758] Step 5:

[0759] The server calculates the optimal fare based on real-time traffic conditions and supply-demand balance, while also considering the user's emotional state, and presents it to the user in the most reassuring way. This result is transmitted to the terminal in real time and displayed to the user.

[0760] Step 6:

[0761] The driver's terminal receives service delivery hints from the server based on the user's emotional information. The driver uses this information to adjust the service during the ride.

[0762] Step 7:

[0763] After the ride is complete, users provide feedback on the driver and the overall service through the application. This feedback is re-analyzed by an emotion engine and used to improve future matching and service.

[0764] (Example 2)

[0765] 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".

[0766] Traditional ride-hailing service systems have struggled to provide a high-quality customer experience without considering the emotional state of users. In particular, when users' emotional states, such as stress and dissatisfaction, affect the service experience, a more flexible and emotionally resonant service is needed. There is a need for methods to address these challenges and improve user satisfaction.

[0767] 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.

[0768] In this invention, the server includes means for receiving the user's current location and destination location information, means for evaluating the user's emotional state from voice and input data, and means for selecting a driver based on the emotional evaluation and adapting to the service provision. This makes it possible to provide considerate service that responds to the user's emotions.

[0769] "Current location information" refers to data that indicates the real-time location of the device being used by the user.

[0770] "Destination location information" is data that indicates the location of the point the user wants to reach.

[0771] "Usage history information" refers to records of past service usage by users, including date and time, route, and driver information.

[0772] "Preference information" refers to data about individual preferences based on the conditions and characteristics of services that users like, as well as past evaluations.

[0773] A "driver" refers to a person who is responsible for transporting users to their destination when providing a ride-hailing service.

[0774] The "optimal route" is the route chosen by the driver to transport passengers to their destination quickly and efficiently.

[0775] "High-demand local information" refers to information about areas where many users need the service at a specific time and place.

[0776] "Emotional state" refers to the user's psychological and emotional condition and is evaluated by changes in voice tone and input speed.

[0777] "Voice and input data" refers to information about voice and text input acquired by the user's device, from which emotional states can be interpreted.

[0778] "Emotional assessment" is the process of analyzing and understanding users' emotions based on collected data.

[0779] "Matching" refers to the process of selecting and connecting the driver best suited to the user's needs.

[0780] The ride-hailing service system of this invention consists of a user, a terminal, and a server. First, the user requests a ride using a smart device. The user's terminal collects emotional information by analyzing location information, voice tone, and input speed. This collected data is transmitted to the server.

[0781] The server receives this data and uses an emotion engine to evaluate the user's emotional state. The emotion engine uses natural language processing and speech analysis technologies to specifically assess the user's emotions. Based on this evaluation, the AI ​​agent selects a driver. For example, if the user is evaluated as feeling stressed, a driver with excellent customer service skills may be selected. In addition, the driver is notified of virtual service hints, enabling them to provide service that takes the user's emotions into consideration.

[0782] As a concrete example, if a user is in a hurry and requests by voice, "I want to arrive on time," the server will recognize the urgency from the tone of voice and the speed of the message. This will then select the driver who can respond most quickly.

[0783] In this system, the generative AI model enables the delivery of services based on user emotions, providing a flexible and personalized ride-hailing experience.

[0784] Example of a prompt

[0785] "Please tell me what kind of considerations are possible when a user is feeling anxious."

[0786] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0787] Step 1:

[0788] The terminal receives ride-hailing requests from users, including destinations and desired times. In addition, it acquires data to infer user emotions, such as voice and text input speed and voice tone. This entire dataset becomes input information for the server.

[0789] Step 2:

[0790] The server receives request data and associated emotion data sent from the terminal. The received data is passed to the emotion engine, which analyzes the user's emotional state using natural language processing and speech analysis techniques. This data processing results in the output of emotional states such as stress, joy, and anger.

[0791] Step 3:

[0792] The server uses an AI agent based on the analysis results of the emotion engine to select the appropriate driver. The selection process considers the evaluated emotional state, the driver's current location, estimated arrival time, and past user ratings to output information on the most suitable driver.

[0793] Step 4:

[0794] The server notifies the selected driver's terminal in real time with hints for providing service that take into account the user's emotional state. For example, it might send a message such as, "The user is feeling stressed, so try to respond calmly." This allows the driver to follow the guidance and respond more appropriately.

[0795] Step 5:

[0796] After the journey is complete, the user provides feedback on the driver and the service provided. This feedback is sent to the server and analyzed again by the sentiment engine. The analyzed feedback becomes important data for improving the service in the future.

[0797] (Application Example 2)

[0798] 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".

[0799] Traditional ride-hailing services did not take into account the emotional state of users, resulting in a uniform service delivery. Therefore, there were limitations in providing a satisfactory ride-hailing experience when users were stressed or seeking relaxation. Furthermore, the matching of drivers and fare presentations did not take users' emotions into consideration, potentially leading to decreased customer satisfaction.

[0800] 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.

[0801] In this invention, the server includes means for receiving the user's current emotional state, means for providing the driver and the user with the optimal route and high-demand area information in real time based on the user's emotional state, and means for calculating and presenting a fare adjusted to the user's emotional state. This makes it possible to provide a flexible ride-hailing service that is attentive to the user's emotions.

[0802] "Means for receiving the user's current emotional state" refers to technology that analyzes voice data and input speed obtained from the user to detect and receive their emotional state in real time.

[0803] "Means for receiving the user's current location and destination information" refers to technology for collecting geographical information of the starting point and destination specified by the user and incorporating it into the system.

[0804] "A means of selecting the optimal driver by analyzing the user's past usage history and preference information" refers to a technology that analyzes past usage history and personal preference data to select the driver best suited to the user.

[0805] "Means of providing drivers and users with real-time optimal routes and information on high-demand areas" refers to a function that provides drivers and users with real-time information on optimal routes and areas with high demand, based on current traffic conditions and demand forecast data.

[0806] "A means of calculating and presenting a fee adjusted to the user's emotional state" refers to a technology that, based on the results of an emotional analysis of the user, sets a fee that takes into account stress reduction and the provision of a sense of security, and then presents this to the user.

[0807] "Means for matching selected drivers with users" refers to a method of efficiently providing services by connecting the most suitable drivers and users based on analyzed information.

[0808] The embodiments for carrying out this invention primarily relate to a server, a user's smart device, and an autonomous vehicle. The server receives voice data and input speed from the user's smart device and detects the user's current emotional state in real time using sentiment analysis technology. This involves converting speech to text using the Google Cloud Speech-to-Text API and then performing sentiment analysis using IBM Watson Natural Language Understanding.

[0809] The user's smart device provides current location and destination information and transmits this information to the server. Furthermore, based on past usage history and preference information, the server analyzes and selects the most suitable driver. The server also utilizes AI models to predict demand in real time and provides drivers and users with the most efficient routes and information on high-demand areas.

[0810] Another important element of this program is fare adjustment based on the user's emotional state. This ensures that the most appropriate service is provided to the user. For example, if a family is going on a weekend trip to the beach and the system determines that they are feeling stressed at the start of the trip, it will play relaxing music or select a route with less traffic.

[0811] An example of a prompt to input into the generating AI model would be, "The user is feeling a little stressed at the start of their trip. Based on the emotion recognition results, how would you adjust the environment to help them relax?" This question asks about the quality of service required based on a specific emotional state.

[0812] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0813] Step 1:

[0814] The user's device acquires the user's voice data and input speed. This data is sent to the server as input. The server uses the Google Cloud Speech-to-Text API to convert the voice data into text and prepares it for analyzing the user's emotional state.

[0815] Step 2:

[0816] The server uses IBM Watson Natural Language Understanding to perform sentiment analysis on text obtained from speech. This analysis detects emotions such as stress, joy, and anger. The analysis results are generated as sentiment tags, which serve as input for the next step.

[0817] Step 3:

[0818] The server receives the user's current location and destination information. This information is combined with past usage history and preference data, and a generative AI model is used to select the most suitable driver. The output is a list of potential optimal drivers.

[0819] Step 4:

[0820] The server provides drivers and users with real-time information on optimal routes and high-demand areas. This decision-making process utilizes AI technology to acquire demand forecasting data and evaluate the convenience of each route. The output provides real-time driving routes and related information.

[0821] Step 5:

[0822] The server calculates a fee adjusted for the user based on their emotional state. If the emotional state indicates stress, adjustments such as discounts are applied. This calculates the optimal fee for the user, and this information is presented to the user. The output is the adjusted fee information.

[0823] Step 6:

[0824] The server matches selected drivers with users. This process utilizes previously obtained sentiment tags and historical information, with the driver's response skills being the evaluation criterion. The selection results are output, and a match is confirmed.

[0825] Step 7:

[0826] After the ride-hailing experience ends, users send feedback about the driver and service to the server via their device. The server analyzes this feedback to improve future services and stores it as new data. The input is feedback information, and the output is trend analysis data as a result of the analysis.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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."

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0848] The following is further disclosed regarding the embodiments described above.

[0849] (Claim 1)

[0850] Means for receiving the user's current location and destination information,

[0851] A means for selecting the optimal driver by analyzing the user's past usage history and preference information,

[0852] A means of providing selected drivers with information on optimal routes and high-demand areas,

[0853] A means of calculating and presenting the optimal price to the user in real time,

[0854] A means of matching selected drivers with users,

[0855] A system that includes this.

[0856] (Claim 2)

[0857] The system according to claim 1, which uses AI technology to predict demand in each area in real time and optimize driver allocation.

[0858] (Claim 3)

[0859] The system according to claim 1, wherein users evaluate drivers and utilize that feedback in subsequent matching processes.

[0860] "Example 1"

[0861] (Claim 1)

[0862] A device that receives the user's geographical location information and destination information,

[0863] A device that analyzes users' past usage records and preference information to select the most suitable driver,

[0864] A device that provides the selected driver with information on the optimal route and high-demand areas,

[0865] A device that calculates and displays the optimal price for the user, taking into account time information,

[0866] A device that connects the selected driver and the user,

[0867] A device that allows users to evaluate the driver after their ride, and uses the results in the analysis process for subsequent rides.

[0868] A system that includes this.

[0869] (Claim 2)

[0870] The system according to claim 1, which uses data processing technology to instantly estimate the demand in each area and optimize the driver arrangement.

[0871] (Claim 3)

[0872] The system according to claim 1, which analyzes information entered from the user's terminal and selects a driver based on their past performance.

[0873] "Application Example 1"

[0874] (Claim 1)

[0875] Means for receiving the user's current location and destination information,

[0876] A method for selecting the most suitable delivery person by analyzing the user's past usage history and preference information,

[0877] A means of providing selected delivery personnel with information on optimal travel routes and high-demand areas,

[0878] A means of calculating and presenting the optimal price to the user in real time,

[0879] A means of matching selected delivery personnel with users,

[0880] A system that includes this.

[0881] (Claim 2)

[0882] The system according to claim 1, which uses AI technology to predict demand in each area in real time and optimizes the allocation of delivery personnel.

[0883] (Claim 3)

[0884] The system according to claim 1, wherein users rate delivery drivers and the feedback is used in the matching process for future deliveries.

[0885] "Example 2 of combining an emotion engine"

[0886] (Claim 1)

[0887] Means for receiving the user's current location and destination location information,

[0888] A means for selecting the optimal driver by analyzing the user's past usage history and preference information,

[0889] A means of providing selected drivers with optimal routes and information on high-demand areas,

[0890] A means for evaluating the emotional state of a user from voice and input data,

[0891] A means of selecting drivers based on emotional evaluation and adapting to this when providing services,

[0892] A means of calculating and presenting the optimal price to the user in real time,

[0893] A means of matching selected drivers with users,

[0894] A system that includes this.

[0895] (Claim 2)

[0896] The system according to claim 1, which uses AI technology to predict demand in each region in real time and optimize driver allocation.

[0897] (Claim 3)

[0898] The system according to claim 1, wherein users evaluate drivers and utilize the feedback in subsequent matching processes.

[0899] "Application example 2 when combining with an emotional engine"

[0900] (Claim 1)

[0901] A means of receiving the user's current emotional state,

[0902] Means for receiving the user's current location and destination information,

[0903] A means for selecting the optimal driver by analyzing the user's past usage history and preference information,

[0904] A means of providing drivers and users with the optimal route and high-demand area information in real time based on the emotional state of the users,

[0905] A means of calculating and presenting a fee adjusted to take into account the user's emotional state,

[0906] A means of matching selected drivers with users,

[0907] A system that includes this.

[0908] (Claim 2)

[0909] The system according to claim 1, which uses AI technology to predict demand in each region in real time, optimizes driver allocation, and provides services based on the emotional state of users.

[0910] (Claim 3)

[0911] The system according to claim 1, wherein users evaluate drivers and utilize the feedback to improve the matching process and services in the future, taking into account emotional states. [Explanation of symbols]

[0912] 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. Means for receiving the user's current location and destination information, A means for selecting the optimal driver by analyzing the user's past usage history and preference information, A means of providing selected drivers with information on optimal routes and high-demand areas, A means of calculating and presenting the optimal price to the user in real time, A means of matching selected drivers with users, A system that includes this.

2. The system according to claim 1, which uses AI technology to predict demand in each area in real time and optimize driver allocation.

3. The system according to claim 1, wherein the user evaluates the driver and the feedback is used in the matching process in subsequent instances.

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