system

Abstract

Provide a system. 【Solution means】 An information collection means for collecting crowd flow information, A prediction means for predicting future transportation demand based on the collected crowd flow information, An arrangement means for appropriately arranging transportation means in a specific area based on the predicted transportation demand, An operation control means for automatically operating the transportation means, A vehicle dispatching means for receiving a transportation request from a user and selecting and dispatching a transportation means according to the transportation request, An input means for providing an interface through which the user can input destination information and desired arrival time, A display means for displaying the optimal route and estimated arrival time in real time, A system including the above.

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 the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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 conventional transportation services, the number of drivers has decreased, and it has become difficult to secure experienced drivers due to the aging of the average age. As a result, it is difficult to arrange appropriate transportation means according to demand, and problems such as a decrease in customer satisfaction and a deterioration in operation efficiency have been regarded as issues. In addition, since it is difficult for new drivers to effectively acquire customers in door-to-door sales, there is a problem that the quality of the entire service deteriorates.

Means for Solving the Problems

[0005] This invention provides a system that collects passenger flow information and uses AI to predict future transportation demand. Based on the predicted demand, it optimally allocates transportation methods and utilizes automated driving technology to achieve efficient operation. Furthermore, it receives transportation requests from users in real time and automatically selects and dispatches the most suitable transportation method. This enables high customer satisfaction regardless of driver experience, and improves the efficiency and quality of transportation services.

[0006] "Human flow information" refers to data about the location and movement of people, indicating the concentration of people and movement patterns in specific areas and times.

[0007] "Data collection means" refers to methods for aggregating pedestrian flow information in real time, and this can include various sensors and communication networks.

[0008] A "predictive tool" is a means of analyzing collected data and using a specific algorithm to predict future demand.

[0009] "Placement means" refers to the means of positioning transportation means and services in appropriate locations according to predicted demand.

[0010] "Operation control means" refers to means for automatically operating a means of transport and controlling its route and speed.

[0011] "User" refers to an individual or group that uses a transportation service, and is typically a customer who requires transportation.

[0012] A "transportation request" refers to a request made by a user when they wish to travel from one location to another.

[0013] "Vehicle dispatching means" refers to the means of selecting and instructing the most suitable means of transportation in response to a user's transportation request.

[0014] "Machine learning algorithm" is an approach for analyzing data, learning patterns, and performing prediction and classification, including deep learning, regression analysis, etc.

[0015] "Real-time information" refers to immediate information regarding the current state or situation, based on which rapid decision-making becomes possible.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Example 2 when 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.

Embodiments for Carrying out the Invention

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the labeled 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 a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the labeled 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 disks (e.g., hard disks), or magnetic tapes, and the like.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention is a system for achieving efficient operation management and optimization in transportation services. This system consists of three components: a server, terminals (autonomous vehicles), and users.

[0038] Server Role

[0039] The server collects passenger flow information and uses it to predict future transportation demand. AI uses deep learning technology to learn from past data and predict future demand, determining the optimal allocation of transportation methods. Based on the generated allocation plan, the server issues movement instructions to individual terminals. Furthermore, it receives transportation requests from users, selects the most suitable terminal, and dispatches it.

[0040] Terminal role

[0041] The terminal is an autonomous vehicle that follows instructions received from the server and heads towards its destination. It transmits location and status information to the server in real time and receives instructions as needed. This enables highly automated operation and efficient transportation.

[0042] User roles

[0043] Users access the service via smartphones or dedicated terminals. When a user requests transportation, a request sent through the application allows the server to determine the user's location and destination, and a suitable vehicle is quickly dispatched. This process is simple for the user, enabling the delivery of a fast and convenient service.

[0044] Specific example

[0045] For example, consider a scenario where many people travel from a specific area to a train station during weekday morning rush hour. The server predicts this demand using historical data and real-time pedestrian flow information, and appropriately places the necessary number of terminals in that area. When a user requests a taxi, the server selects the most suitable terminal and dispatches it to the user's location. Once the terminal arrives, the user is safely and comfortably transported to their destination, the train station.

[0046] Thus, the present invention can support the efficient operation of transportation means and provide users with a high level of satisfaction.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The server collects pedestrian flow information received from smartphones and other communication devices. This information includes location data and movement patterns.

[0050] Step 2:

[0051] The server uses AI to analyze collected pedestrian flow information and predict future transportation demand. This prediction utilizes historical trends and real-time data.

[0052] Step 3:

[0053] The server plans the optimal placement of transportation devices (terminals) based on predicted demand patterns. Specifically, it determines how many terminals should be placed in each region.

[0054] Step 4:

[0055] The server sends movement instructions to each terminal to a designated area based on the deployment plan. The terminal then begins automatic operation according to these instructions.

[0056] Step 5:

[0057] Users request taxi services using a dedicated application. The app is used to send information about the departure and destination locations to the server.

[0058] Step 6:

[0059] The server receives the user's request and selects the optimal device based on the user's location and the real-time availability of devices.

[0060] Step 7:

[0061] The server instructs the selected terminal to move towards the user's starting point. The terminal then moves in accordance with this instruction and rushes to the user's location.

[0062] Step 8:

[0063] Once the terminal arrives, the user boards it and travels to their destination. The terminal continuously receives updates from the server while en route to its destination.

[0064] Step 9:

[0065] The terminal transmits data obtained during operation (e.g., travel route, traffic conditions, etc.) to the server in real time, and the server uses this data to make future predictions.

[0066] (Example 1)

[0067] 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."

[0068] In modern transportation services, a significant mismatch between supply and demand is common, making it difficult for users to secure their reserved transportation. Efficient dispatching is particularly challenging during peak hours and in congested areas, leading to decreased operational efficiency of transportation equipment. Furthermore, there is a need for flexible systems that can respond quickly to real-time demand fluctuations.

[0069] 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.

[0070] In this invention, the server includes information gathering means, prediction means, placement determination means, automatic control means, vehicle dispatch determination means, and command transmission means. This enables efficient transportation provision by accurately predicting transportation demand and appropriately arranging transportation equipment. Furthermore, it enables real-time dispatch and operation control of transportation equipment according to the situation, thereby improving user convenience.

[0071] "Information gathering means" refers to a device or system that has the function of collecting human flow information and storing it in a database.

[0072] A "predictive means" is a device or system that has the function of predicting future transportation demand based on collected human flow information.

[0073] "Deployment determination means" refers to a device or system that has the function of efficiently deploying transportation equipment to a specific area in accordance with predicted transportation demand.

[0074] "Automatic control means" refers to a device or system that has the function of automatically operating transportation equipment to its destination.

[0075] A "vehicle dispatch determination means" is a device or system that has the function of selecting the most suitable transportation equipment in response to a transportation request from a user and executing the dispatch.

[0076] "Command transmission means" refers to a device or system that has the function of transmitting movement instructions and control commands generated by a server to transport equipment.

[0077] To implement this invention, a system is required in which three entities—a server, a terminal, and a user—work together. The server collects pedestrian flow information from each region as an information gathering means and records it in a database. This includes GPS data obtained from mobile devices and data acquired from sensors installed in the infrastructure. As an inference means, the server processes the collected data with a generative AI model to predict future transportation demand. This processing uses a deep learning framework such as TENSORFLOW® or PyTorch.

[0078] As a concrete example, the server performs this demand forecast by providing a prompt to the AI ​​model, such as "Predict the demand for next Monday morning based on past travel patterns." Based on the forecast results, the server determines the placement of transportation equipment so that it can be used most efficiently as a means of placement determination.

[0079] The terminal receives commands from the server as an automatic control mechanism and operates the autonomous vehicle, which is a form of transportation equipment, safely and effectively. This operation is carried out while recognizing the surrounding environment using cameras and LiDAR sensors. It is also equipped with a means to transmit real-time location information to the server and receive new commands as needed.

[0080] Users access the dispatch system using a dedicated application. Through the app, they can enter their current location and destination and summon a vehicle at a convenient time. The server receives this request, selects the most suitable vehicle, and dispatches the vehicle. This allows users to travel efficiently and conveniently.

[0081] Through the specific methods described above, this invention makes it possible to realize the smooth and efficient transportation system originally envisioned.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The server collects real-time pedestrian flow information from various regions using data collection methods. Inputs include location data and movement history from mobile devices and infrastructure sensors. This input data is aggregated by the server and stored in a database. The output is a dataset of the latest pedestrian flow information.

[0085] Step 2:

[0086] The server uses a generative AI model as an inference tool to predict future transportation demand from collected pedestrian flow information. In this step, the dataset serves as input, and the AI ​​model is given the prompt "Predict tomorrow's transportation demand based on past movement patterns," and demand forecasting is performed through learning. The model recognizes patterns and analyzes demand fluctuations, generating predicted demand data as output.

[0087] Step 3:

[0088] The server calculates the most efficient placement of transport equipment based on predicted demand data using a placement determination mechanism. Demand data is the input, and the server uses an optimization algorithm to determine where to place the transport equipment. The output is a placement plan, which serves as the basis for sending subsequent commands.

[0089] Step 4:

[0090] The server sends movement instructions to each terminal via a command transmission device. The deployment plan is the input, and the server sends instructions to the autonomous vehicles including the destination, recommended route, and estimated arrival time. As output, each terminal receives instructions to begin movement.

[0091] Step 5:

[0092] The terminal uses automatic control mechanisms to move towards its destination based on commands. Command information from the server is input, and the terminal operates its autonomous control system to drive safely and efficiently. Using real-time data from sensors, it moves while confirming its own position and surrounding environment, and as output, it safely arrives at its destination.

[0093] Step 6:

[0094] Users utilize a vehicle dispatch system and submit transportation requests using a smartphone app. Inputs include user location information and destination data, which are sent to the server via the app. The server receives this data and provides the most suitable vehicle. Outputs include the estimated arrival time and vehicle information of the selected vehicle for the user.

[0095] Step 7:

[0096] The server selects the optimal transport vehicle using real-time data based on the vehicle dispatching decision mechanism. The user's request data and the location information of the transport vehicle are used as input, and the server performs analysis to quickly and appropriately dispatch the vehicle. As output, the selected transport vehicle moves to the user's location.

[0097] (Application Example 1)

[0098] 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."

[0099] Existing transportation services often struggle to cope with fluctuating demand during peak commuting hours, leading to stress for users. Furthermore, optimizing transportation methods in real-time to meet demand is difficult, making efficient dispatching and operation a challenge.

[0100] 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.

[0101] In this invention, the server includes information gathering means, prediction means, arrangement means, operation control means, vehicle dispatch means, input means, and display means. This enables the optimal arrangement of transportation means according to user demand and the provision of efficient routes based on destination information and desired arrival time.

[0102] "Information gathering means" refers to systems and devices that collect information on human movement and past transportation data to help forecast future demand.

[0103] A "predictive tool" is a system or device that uses machine learning algorithms to analyze collected data and predict future transportation demand.

[0104] "Deployment means" refers to systems or devices for appropriately deploying means of transport to specific areas based on predicted demand.

[0105] "Operation control means" refers to systems or devices that perform control to automatically operate means of transport.

[0106] A "vehicle dispatching system" is a system or device that selects and dispatches the most suitable means of transportation in response to a user's transportation request.

[0107] An "input method" refers to a system or device that provides an interface for users to input destination information and desired arrival time.

[0108] A "display means" refers to a system or device that displays the optimal route and estimated arrival time to the user in real time.

[0109] To realize this invention, a system is required in which three entities—a server, a terminal, and a user—work in cooperation. The server uses AWS® Lambda or API Gateway to execute a program that integrates information gathering, prediction, placement, operation control, and dispatching functions. A generative AI model using TensorFlow learns from past pedestrian flow and transportation data to predict future demand. Based on this, the server determines the optimal placement of transportation and sends instructions to the terminal in real time.

[0110] The terminal is an autonomous vehicle that operates by receiving instructions from a server. It transmits location information and status data to the server in real time and optimizes the route as needed. This system enables fast and efficient transportation for users.

[0111] Users access the service using an application installed on their smartphones. They send destination information and desired arrival time to the server via input methods to receive the most suitable vehicle. The application is built with Flutter® and displays the optimal route and estimated arrival time in real time.

[0112] As a concrete example, suppose a user wants to arrive at work at 8 AM. By entering the desired time in the application beforehand, the server analyzes this information and dispatches the most suitable autonomous vehicle. The route and estimated arrival time are displayed in the app in real time, ensuring a stable service experience for the user.

[0113] An example of a prompt for a generated AI model is: "I want to arrive at the office by 8:00 AM on a weekday. Based on past traffic data and pedestrian flow patterns, please suggest the best autonomous vehicle and route for me." Through this prompt, the service will be provided appropriately.

[0114] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0115] Step 1:

[0116] The server collects historical and real-time pedestrian flow data through various information gathering methods. This data is stored in a large database and used for subsequent analysis. This data collection is a crucial step that forms the basis for highly accurate demand forecasting.

[0117] Step 2:

[0118] The server uses prediction tools to forecast future transportation demand based on collected data. In this process, a generative AI model using TensorFlow analyzes the data and identifies demand patterns. The output includes peak demand times and the number of transportation options needed in a specific region.

[0119] Step 3:

[0120] The server uses deployment methods to develop a plan for deploying transportation to specific areas to meet predicted demand. This deployment plan is updated in real time, and instructions are sent from the server to each terminal. As a result, efficient deployment of transportation becomes possible.

[0121] Step 4:

[0122] The terminal automatically starts operating based on instructions sent from the server. The terminal constantly transmits its location and operating status to the server, which then uses this data to optimize the operating route as needed. This enables optimal real-time operation.

[0123] Step 5:

[0124] Users enter destination information and desired arrival time into a smartphone app using an input device. The entered information is sent to a server, which then selects the most suitable vehicle and dispatches it. This enables a very simple and efficient service for users.

[0125] Step 6:

[0126] The server transmits information through a display system to the user, showing the optimal route and estimated arrival time in real time via the application. This information is immediately reflected in the app, allowing the user to confidently head to the customer's location according to their schedule.

[0127] Step 7:

[0128] Users provide regular feedback, and the server uses this feedback to further improve the service. Using example prompts, the generated AI model can suggest transportation options that meet the user's evolving needs. Based on a prompt such as, "I want to arrive at the office by 8:00 AM on a weekday. Please suggest the best autonomous vehicle and route for me based on past traffic data and pedestrian flow patterns," the optimal transportation service will continue to be provided.

[0129] 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.

[0130] This invention is a system for achieving efficient operation and service provision in transportation services while taking user emotions into consideration. This system consists of four components: a server, a terminal (autonomous vehicle), a user, and an emotion engine.

[0131] Server Role

[0132] The server collects passenger flow information and uses AI to predict future transportation demand. Furthermore, it optimizes services based on user emotions using emotional data provided by an emotion engine. Specifically, it plans the allocation of transportation methods and manages their operation. It also receives transportation requests from users and selects and dispatches the most suitable terminal.

[0133] Terminal role

[0134] The terminal operates as an autonomous vehicle, automatically running based on instructions from the server. By transmitting real-time operational data and the user's riding status to the server, it contributes to optimizing subsequent schedules. Following instructions from an emotion engine, it can also make environmental adjustments to improve the user experience during the ride.

[0135] User roles

[0136] Users request services via smartphones or other devices, providing the server with information about their location, destination, and needs. In addition, an emotion engine analyzes the user's voice and facial expressions to detect multiple emotional states.

[0137] The role of the emotional engine

[0138] The emotion engine analyzes the user's emotions in real time using user voice data and facial recognition technology. Based on the detected emotions, the server obtains new information and adjusts the transportation service accordingly. This ensures that the user receives appropriate in-vehicle environment settings and friendly service.

[0139] Specific example

[0140] For example, if the emotion engine determines that a user is experiencing stress during their morning commute on a weekday, the server uses this information to send instructions to the terminal to optimize the music playing or the in-car temperature. As a result, the user can travel to their destination more comfortably. This process significantly improves the quality of transportation services and makes it possible to provide users with a high level of satisfaction.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] The server collects pedestrian flow information from smartphones and other communication devices, and simultaneously trains an AI model using historical transportation data. This allows it to predict future transportation demand.

[0144] Step 2:

[0145] The server determines which terminals should be placed in which areas based on predicted demand data, and sends this plan to each terminal. The terminals then move to the designated areas and prepare accordingly.

[0146] Step 3:

[0147] Users request transportation services using a dedicated app. The request includes information such as the departure point, destination, and desired departure time. This information is sent to the server.

[0148] Step 4:

[0149] The emotion engine analyzes the user's facial recognition data and voice input in real time to determine the user's emotional state. This data is then sent to the server.

[0150] Step 5:

[0151] The server selects the most suitable terminal and issues dispatch instructions based on the user's request and emotional state. Selection criteria include travel time, distance, and the ability to provide service tailored to the user's emotional state.

[0152] Step 6:

[0153] The terminal receives instructions and moves towards the user's location. Upon arrival, it picks up the user and boarding begins.

[0154] Step 7:

[0155] Based on instructions from the emotion engine, the device adjusts music, temperature settings, and other parameters to ensure the user's comfort as it heads towards its destination.

[0156] Step 8:

[0157] While in transit, the terminal continuously transmits real-time operational data and user status to the server, ensuring that the latest operational status is always reflected.

[0158] Step 9:

[0159] Upon arrival at the destination, the terminal allows the user to disembark and sends data about the completed trip to the server. This data is used to optimize future trips and improve services.

[0160] (Example 2)

[0161] 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".

[0162] This invention aims to provide a more comfortable and efficient transportation experience by considering the emotions of users in real time during transportation services. Conventional transportation systems have been unable to consider user emotions in demand forecasting and vehicle dispatching, leading to situations where users experience anxiety and stress. Therefore, there has been a need for new transportation services that utilize emotional information to improve user satisfaction.

[0163] 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.

[0164] In this invention, the server includes information gathering means, demand forecasting means, and placement determination means. This enables accurate demand forecasting that incorporates user sentiment information and optimal placement of transportation functions.

[0165] "Information gathering means" refers to methods for collecting various data related to transportation systems, including data on user trends and sentiment.

[0166] "Demand forecasting methods" refer to algorithms and technologies used to predict future transportation demand based on collected pedestrian flow data and sentiment information.

[0167] A "location determination means" is a means for positioning transportation functions at the optimal location, taking into account predicted transportation demand and sentiment information.

[0168] "Operation control means" refers to means for operating transportation functions automatically and efficiently, utilizing autonomous driving technology to safely transport goods to their destinations.

[0169] A "vehicle dispatch management system" is a means of selecting appropriate transportation functions based on user requests and emotional information, and dispatching transportation functions in the most optimal way for the user.

[0170] "Analysis methods" refer to technologies and methods that analyze voice data and facial recognition data obtained from users to evaluate their emotional state in real time.

[0171] This system is designed to provide efficient services in the transportation sector that take user emotions into consideration. Its main components consist of a server, terminals, users, and an emotion engine.

[0172] Server Role

[0173] The server operates on a cloud platform and uses data collection tools to gather pedestrian flow and sentiment information from users. The server utilizes AWS cloud services and uses a generative AI model based on this data to forecast demand. The demand forecasting tool processes this information and uses machine learning algorithms to predict future transportation demand. The collected data includes location information, time of day, and historical transportation data. Furthermore, the server uses a deployment decision tool to optimally allocate transportation functions, responding quickly to user requests.

[0174] Terminal role

[0175] The autonomous vehicles used as terminals utilize advanced operational control systems to safely and efficiently transport users to their destinations. Leveraging technologies such as NVIDIA DRIVE, the terminals autonomously calculate routes and optimize their operation through real-time data communication with servers. Furthermore, they are equipped with a function to adjust the in-vehicle environment according to the emotional state of the passengers. For example, playing relaxing music can reduce user stress.

[0176] User roles

[0177] Users request transportation services through a smartphone app. The app allows them to input their location, destination, and even special needs, and send this information to the server. An emotion engine analyzes the user's voice and face, acquiring emotion data in real time. This analysis is then transferred to the server as data and used to optimize the service.

[0178] The role of the emotional engine

[0179] The emotion engine uses analytical tools to analyze user voice and facial recognition data, evaluating emotional trends in real time. It utilizes AI services such as Google Cloud to precisely detect the user's emotional state. Based on this information, the server fine-tunes the transport service, providing an environment and service tailored to the user.

[0180] Examples of specific cases and prompt statements

[0181] If a user is experiencing stress during weekday morning commutes, the server, based on data from the emotion engine, instructs the device to provide a relaxing environment. As a result, users can reach their destination with reduced stress, leading to improved service satisfaction.

[0182] Example prompt: "Forecast current transportation demand and suggest which areas to deploy terminals in."

[0183] Example prompt: "Based on user sentiment data, please tell us how to optimize the service during the ride."

[0184] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0185] Step 1:

[0186] A user requests a transportation service using a smartphone app. As input, the user enters their current location, destination, and special needs into the app. The app also collects the user's current emotional state using voice and facial recognition. As output, all this information is sent to a server. In terms of specific actions, the user operates the app and enters the necessary data according to the instructions.

[0187] Step 2:

[0188] The server processes the received information using data collection tools. Input includes user information and sentiment data. A generating AI model analyzes this data to predict future transportation demand. The output generates prediction results and user sentiment information. Specifically, the server applies machine learning algorithms to compare current data with past data and analyze demand patterns.

[0189] Step 3:

[0190] The server uses demand forecasting to make appropriate placement decisions. It uses previously generated demand forecasts and sentiment information as input. This determines the optimal terminal placement, which is then sent to the terminals as instructions. Output includes placement and operational plans. Specifically, the server optimizes the terminal placement plan while considering traffic conditions and user sentiment.

[0191] Step 4:

[0192] The terminal travels along a designated route based on instructions from the server. It uses the deployment plan and operational instructions received from the server as input. The automated driving system within the terminal analyzes this data and generates real-time operational data. The operational data and the user's ride experience are sent to the server as output. Specifically, the terminal takes in GPS data and real-time traffic information to select the optimal route.

[0193] Step 5:

[0194] The emotion engine continuously monitors the user's emotional state while they are riding. It uses facial recognition data and voice data as input. This data is analyzed to detect changes in the user's emotions in real time. The emotion analysis results are sent to the server as output and used to fine-tune the service. Specifically, the emotion engine uses AI technology to evaluate the user's stress and relaxation levels while they are riding.

[0195] (Application Example 2)

[0196] 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".

[0197] Traditional transportation services focus on meeting users' physical transportation needs and lack the technology to provide services tailored to users' emotional states. Therefore, there is a need to individually optimize the transportation environment based on users' emotional states to provide more comfortable and satisfying transportation services.

[0198] 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.

[0199] In this invention, the server includes emotion analysis means for analyzing emotional states, environment setting means for adjusting the in-vehicle environment of the transportation means based on the analyzed emotional states, and prediction means for collecting passenger flow information and predicting future transportation demand. This makes it possible to provide individualized services tailored to the emotional states of users.

[0200] "Personnel flow information" refers to data about the movement of potential people using transportation services.

[0201] "Data collection means" refers to a device or method for collecting information on human flow and data on users.

[0202] A "predictive tool" is a system that estimates future transportation demand based on collected data.

[0203] "Deployment method" refers to a method of appropriately deploying transportation means in a specific area in accordance with predicted transportation demand.

[0204] "Operation control means" refers to a device or control system for automatically operating a means of transport.

[0205] A "vehicle dispatch system" is a system that selects and dispatches the most suitable mode of transportation according to the user's request.

[0206] "Emotional analysis means" refers to a system or method that analyzes a user's emotional state in real time.

[0207] "Environment setting means" refers to a device or system that adjusts the in-vehicle environment of a means of transport based on the analyzed emotional state.

[0208] This invention is a system for providing transportation services that takes into account the emotions of users and offers an efficient and comfortable environment. The system mainly consists of a server, terminals, users, and emotion analysis means.

[0209] The server acquires pedestrian flow information using data collection methods and predicts future transportation demand using machine learning algorithms based on this data. In particular, by comparing this data with past transportation data, it is possible to predict fluctuations in demand with high accuracy. Furthermore, based on the prediction results, the server optimally deploys transportation means using deployment means and efficiently operates autonomous vehicles using operation control means.

[0210] The terminal functions as an autonomous vehicle, transmitting real-time operational data to a server. It also reflects the user's emotional state, acquired through emotion analysis, in the in-vehicle environment. This process utilizes voice and facial recognition technologies; for example, using Amazon Rekognition or Google Cloud Vision enables accurate emotion detection. Through environment settings, a comfortable in-vehicle experience tailored to the user's emotional state is provided.

[0211] Users request transportation services using their smartphones, providing location information and destinations. A sentiment analysis system installed on the device analyzes the user's voice and facial expressions. This allows the user's real-time emotional state to be transmitted to the server, which then provides the most appropriate service. An example of a prompt might be, "The user wants to relax now. Please recommend some music."

[0212] For example, if a user is feeling stressed during their commute, the server can send a command to the device to play relaxing music in the car. In this way, it is possible to provide users with a more comfortable travel experience and improve the quality of transportation services.

[0213] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0214] Step 1:

[0215] The server acquires pedestrian flow information using data collection methods and stores this information in a database. The input is location data obtained from various sensors and GPS devices, and the output is an integrated pedestrian flow database. This data is used for subsequent demand forecasting.

[0216] Step 2:

[0217] The server uses machine learning algorithms to analyze human flow information in a database and predict future transportation demand. The input is historical human flow data, and the output is a predicted demand model. The data calculation here involves extracting demand patterns that take into account temporal fluctuations.

[0218] Step 3:

[0219] The server generates an appropriate transportation allocation plan based on predicted transportation demand. Based on the prediction results, it determines how many vehicles are needed in a specific area and sends that information to the transportation providers. The input is a demand model, and the output is a specific vehicle allocation plan.

[0220] Step 4:

[0221] The terminal initiates automated operation control and operates based on the deployment plan received from the server. The input is the vehicle deployment plan, and the output is real-time operation data. Position tracking and dynamic route correction of the vehicles are performed using sensors.

[0222] Step 5:

[0223] Users submit transportation requests via a smartphone app, providing location and destination information. Input is the user's request data, and output is the request data sent to the server. The user's needs are registered in their user profile.

[0224] Step 6:

[0225] The server analyzes the user's voice and facial expression data using emotion analysis tools. The input is the user's voice and image data, and the output is the analyzed emotional state. A generative AI model is applied, utilizing prompt text to identify emotions.

[0226] Step 7:

[0227] The device executes instructions to adjust the in-car environment based on the acquired emotional state. The input is the analyzed emotional state, and the output is the adjusted in-car environment data. Specific actions include music selection and temperature adjustment.

[0228] Step 8:

[0229] The server monitors the situation in real time and makes adjustments to the operation plan and in-vehicle environment as needed. Inputs are real-time operation data and in-vehicle environment data, and output is optimized service delivery. A feedback loop is formed, improving the quality of service.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] [Second Embodiment]

[0234] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0235] 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.

[0236] 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).

[0237] 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.

[0238] 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.

[0239] 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).

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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".

[0246] This invention is a system for achieving efficient operation management and optimization in transportation services. This system consists of three components: a server, terminals (autonomous vehicles), and users.

[0247] Server Role

[0248] The server collects passenger flow information and uses it to predict future transportation demand. AI uses deep learning technology to learn from past data and predict future demand, determining the optimal allocation of transportation methods. Based on the generated allocation plan, the server issues movement instructions to individual terminals. Furthermore, it receives transportation requests from users, selects the most suitable terminal, and dispatches it.

[0249] Terminal role

[0250] The terminal is an autonomous vehicle that follows instructions received from the server and heads towards its destination. It transmits location and status information to the server in real time and receives instructions as needed. This enables highly automated operation and efficient transportation.

[0251] User roles

[0252] Users access the service via smartphones or dedicated terminals. When a user requests transportation, a request sent through the application allows the server to determine the user's location and destination, and a suitable vehicle is quickly dispatched. This process is simple for the user, enabling the delivery of a fast and convenient service.

[0253] Specific example

[0254] For example, consider a scenario where many people travel from a specific area to a train station during weekday morning rush hour. The server predicts this demand using historical data and real-time pedestrian flow information, and appropriately places the necessary number of terminals in that area. When a user requests a taxi, the server selects the most suitable terminal and dispatches it to the user's location. Once the terminal arrives, the user is safely and comfortably transported to their destination, the train station.

[0255] Thus, the present invention can support the efficient operation of transportation means and provide users with a high level of satisfaction.

[0256] The following describes the processing flow.

[0257] Step 1:

[0258] The server collects pedestrian flow information received from smartphones and other communication devices. This information includes location data and movement patterns.

[0259] Step 2:

[0260] The server uses AI to analyze collected pedestrian flow information and predict future transportation demand. This prediction utilizes historical trends and real-time data.

[0261] Step 3:

[0262] The server plans the optimal placement of transportation devices (terminals) based on predicted demand patterns. Specifically, it determines how many terminals should be placed in each region.

[0263] Step 4:

[0264] The server sends movement instructions to each terminal to a designated area based on the deployment plan. The terminal then begins automatic operation according to these instructions.

[0265] Step 5:

[0266] Users request taxi services using a dedicated application. The app is used to send information about the departure and destination locations to the server.

[0267] Step 6:

[0268] The server receives the user's request and selects the optimal device based on the user's location and the real-time availability of devices.

[0269] Step 7:

[0270] The server instructs the selected terminal to move towards the user's starting point. The terminal then moves in accordance with this instruction and rushes to the user's location.

[0271] Step 8:

[0272] Once the terminal arrives, the user boards it and travels to their destination. The terminal continuously receives updates from the server while en route to its destination.

[0273] Step 9:

[0274] The terminal transmits data obtained during operation (e.g., travel route, traffic conditions, etc.) to the server in real time, and the server uses this data to make future predictions.

[0275] (Example 1)

[0276] 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."

[0277] In modern transportation services, a significant mismatch between supply and demand is common, making it difficult for users to secure their reserved transportation. Efficient dispatching is particularly challenging during peak hours and in congested areas, leading to decreased operational efficiency of transportation equipment. Furthermore, there is a need for flexible systems that can respond quickly to real-time demand fluctuations.

[0278] 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.

[0279] In this invention, the server includes information gathering means, prediction means, placement determination means, automatic control means, vehicle dispatch determination means, and command transmission means. This enables efficient transportation provision by accurately predicting transportation demand and appropriately arranging transportation equipment. Furthermore, it enables real-time dispatch and operation control of transportation equipment according to the situation, thereby improving user convenience.

[0280] The "information collection means" is a device or system having a function of collecting people flow information and storing it in a database.

[0281] The "estimation means" is a device or system having a function of predicting future transportation demand based on the collected people flow information.

[0282] The "placement determination means" is a device or system having a function of efficiently placing transportation equipment in a specific area according to the predicted transportation demand.

[0283] The "automatic control means" is a device or system having a function of automatically operating transportation equipment to a destination.

[0284] The "vehicle allocation determination means" is a device or system having a function of receiving a transportation request from a user, selecting an optimal transportation equipment, and executing vehicle allocation.

[0285] The "command transmission means" is a device or system having a function of transmitting a movement instruction or a control command generated by a server to transportation equipment.

[0286] To implement this invention, a system in which three entities, namely a server, a terminal, and a user, cooperate is required. The server collects people flow information in each area as information collection means and records it in a database. This includes GPS data obtained from mobile devices and data acquired from sensors installed in infrastructure. The server processes the collected data with a generated AI model as estimation means and predicts future transportation demand. Deep learning frameworks such as TensorFlow or PyTorch are used for this processing.

[0287] As a concrete example, the server performs this demand forecast by providing a prompt to the AI ​​model, such as "Predict the demand for next Monday morning based on past travel patterns." Based on the forecast results, the server determines the placement of transportation equipment so that it can be used most efficiently as a means of placement determination.

[0288] The terminal receives commands from the server as an automatic control mechanism and operates the autonomous vehicle, which is a form of transportation equipment, safely and effectively. This operation is carried out while recognizing the surrounding environment using cameras and LiDAR sensors. It is also equipped with a means to transmit real-time location information to the server and receive new commands as needed.

[0289] Users access the dispatch system using a dedicated application. Through the app, they can enter their current location and destination and summon a vehicle at a convenient time. The server receives this request, selects the most suitable vehicle, and dispatches the vehicle. This allows users to travel efficiently and conveniently.

[0290] Through the specific methods described above, this invention makes it possible to realize the smooth and efficient transportation system originally envisioned.

[0291] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0292] Step 1:

[0293] The server collects real-time pedestrian flow information from various regions using data collection methods. Inputs include location data and movement history from mobile devices and infrastructure sensors. This input data is aggregated by the server and stored in a database. The output is a dataset of the latest pedestrian flow information.

[0294] Step 2:

[0295] The server uses a generative AI model as an inference tool to predict future transportation demand from collected pedestrian flow information. In this step, the dataset serves as input, and the AI ​​model is given the prompt "Predict tomorrow's transportation demand based on past movement patterns," and demand forecasting is performed through learning. The model recognizes patterns and analyzes demand fluctuations, generating predicted demand data as output.

[0296] Step 3:

[0297] The server calculates the most efficient placement of transport equipment based on predicted demand data using a placement determination mechanism. Demand data is the input, and the server uses an optimization algorithm to determine where to place the transport equipment. The output is a placement plan, which serves as the basis for sending subsequent commands.

[0298] Step 4:

[0299] The server sends movement instructions to each terminal via a command transmission device. The deployment plan is the input, and the server sends instructions to the autonomous vehicles including the destination, recommended route, and estimated arrival time. As output, each terminal receives instructions to begin movement.

[0300] Step 5:

[0301] The terminal uses automatic control mechanisms to move towards its destination based on commands. Command information from the server is input, and the terminal operates its autonomous control system to drive safely and efficiently. Using real-time data from sensors, it moves while confirming its own position and surrounding environment, and as output, it safely arrives at its destination.

[0302] Step 6:

[0303] The user utilizes the vehicle allocation determination means to send a transportation request using a smartphone app. As inputs, there is user location information and destination data, which are sent to the server through the app. The server receives this data and provides an optimal vehicle allocation. The output is the scheduled arrival time of the selected transportation device and vehicle information for the user.

[0304] Step 7:

[0305] Based on the vehicle allocation determination means, the server uses real-time data to select an optimal transportation device. The user's request data and the location information of the transportation devices are inputs, and the server performs analysis to execute a quick and appropriate vehicle allocation. As output, the selected transportation device moves towards the user's location.

[0306] (Application Example 1)

[0307] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0308] Existing transportation services are difficult to cope with demand fluctuations during commuting peak hours and often cause stress to users. Also, due to the difficulty of optimizing transportation means in real-time according to demand, efficient vehicle allocation and operation have become issues.

[0309] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0310] In this invention, the server includes an information collection means, a prediction means, an arrangement means, an operation control means, a vehicle allocation means, an input means, and a display means. Thereby, it becomes possible to optimally arrange transportation means according to the needs of users and provide an efficient route based on destination information and the desired arrival time.

[0311] The "information collection means" is a system or device that collects pedestrian flow information and past transportation data to be used for future demand prediction.

[0312] A "predictive tool" is a system or device that uses machine learning algorithms to analyze collected data and predict future transportation demand.

[0313] "Deployment means" refers to systems or devices for appropriately deploying means of transport to specific areas based on predicted demand.

[0314] "Operation control means" refers to systems or devices that perform control to automatically operate means of transport.

[0315] A "vehicle dispatching system" is a system or device that selects and dispatches the most suitable means of transportation in response to a user's transportation request.

[0316] An "input method" refers to a system or device that provides an interface for users to input destination information and desired arrival time.

[0317] A "display means" refers to a system or device that displays the optimal route and estimated arrival time to the user in real time.

[0318] To realize this invention, a system is required in which three entities—a server, a terminal, and a user—work in cooperation. The server uses AWS Lambda or API Gateway to execute a program that integrates information gathering, prediction, placement, operation control, and dispatching functions. A generative AI model using TensorFlow learns from past pedestrian flow and transportation data to predict future demand. Based on this, the server determines the optimal placement of transportation and sends instructions to the terminal in real time.

[0319] The terminal is an autonomous vehicle that operates by receiving instructions from a server. It transmits location information and status data to the server in real time and optimizes the route as needed. This system enables fast and efficient transportation for users.

[0320] Users access the service using an application installed on their smartphones. They send destination information and desired arrival time to the server via input methods to receive the most suitable ride. The application is built with Flutter and displays the best route and estimated arrival time in real time.

[0321] As a concrete example, suppose a user wants to arrive at work at 8 AM. By entering the desired time in the application beforehand, the server analyzes this information and dispatches the most suitable autonomous vehicle. The route and estimated arrival time are displayed in the app in real time, ensuring a stable service experience for the user.

[0322] An example of a prompt for a generated AI model is: "I want to arrive at the office by 8:00 AM on a weekday. Based on past traffic data and pedestrian flow patterns, please suggest the best autonomous vehicle and route for me." Through this prompt, the service will be provided appropriately.

[0323] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0324] Step 1:

[0325] The server collects historical and real-time pedestrian flow data through various information gathering methods. This data is stored in a large database and used for subsequent analysis. This data collection is a crucial step that forms the basis for highly accurate demand forecasting.

[0326] Step 2:

[0327] The server uses prediction tools to forecast future transportation demand based on collected data. In this process, a generative AI model using TensorFlow analyzes the data and identifies demand patterns. The output includes peak demand times and the number of transportation options needed in a specific region.

[0328] Step 3:

[0329] The server uses deployment methods to develop a plan for deploying transportation to specific areas to meet predicted demand. This deployment plan is updated in real time, and instructions are sent from the server to each terminal. As a result, efficient deployment of transportation becomes possible.

[0330] Step 4:

[0331] The terminal automatically starts operating based on instructions sent from the server. The terminal constantly transmits its location and operating status to the server, which then uses this data to optimize the operating route as needed. This enables optimal real-time operation.

[0332] Step 5:

[0333] Users enter destination information and desired arrival time into a smartphone app using an input device. The entered information is sent to a server, which then selects the most suitable vehicle and dispatches it. This enables a very simple and efficient service for users.

[0334] Step 6:

[0335] The server transmits information through a display system to the user, showing the optimal route and estimated arrival time in real time via the application. This information is immediately reflected in the app, allowing the user to confidently head to the customer's location according to their schedule.

[0336] Step 7:

[0337] Users provide regular feedback, and the server uses this feedback to further improve the service. Using example prompts, the generated AI model can suggest transportation options that meet the user's evolving needs. Based on a prompt such as, "I want to arrive at the office by 8:00 AM on a weekday. Please suggest the best autonomous vehicle and route for me based on past traffic data and pedestrian flow patterns," the optimal transportation service will continue to be provided.

[0338] 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.

[0339] This invention is a system for achieving efficient operation and service provision in transportation services while taking user emotions into consideration. This system consists of four components: a server, a terminal (autonomous vehicle), a user, and an emotion engine.

[0340] Server Role

[0341] The server collects passenger flow information and uses AI to predict future transportation demand. Furthermore, it optimizes services based on user emotions using emotional data provided by an emotion engine. Specifically, it plans the allocation of transportation methods and manages their operation. It also receives transportation requests from users and selects and dispatches the most suitable terminal.

[0342] Terminal role

[0343] The terminal operates as an autonomous vehicle, automatically running based on instructions from the server. By transmitting real-time operational data and the user's riding status to the server, it contributes to optimizing subsequent schedules. Following instructions from an emotion engine, it can also make environmental adjustments to improve the user experience during the ride.

[0344] User roles

[0345] Users request services via smartphones or other devices, providing the server with information about their location, destination, and needs. In addition, an emotion engine analyzes the user's voice and facial expressions to detect multiple emotional states.

[0346] The role of the emotional engine

[0347] The emotion engine analyzes the user's emotions in real time using user voice data and facial recognition technology. Based on the detected emotions, the server obtains new information and adjusts the transportation service accordingly. This ensures that the user receives appropriate in-vehicle environment settings and friendly service.

[0348] Specific example

[0349] For example, if the emotion engine determines that a user is experiencing stress during their morning commute on a weekday, the server uses this information to send instructions to the terminal to optimize the music playing or the in-car temperature. As a result, the user can travel to their destination more comfortably. This process significantly improves the quality of transportation services and makes it possible to provide users with a high level of satisfaction.

[0350] The following describes the processing flow.

[0351] Step 1:

[0352] The server collects pedestrian flow information from smartphones and other communication devices, and simultaneously trains an AI model using historical transportation data. This allows it to predict future transportation demand.

[0353] Step 2:

[0354] The server determines which terminals should be placed in which areas based on predicted demand data, and sends this plan to each terminal. The terminals then move to the designated areas and prepare accordingly.

[0355] Step 3:

[0356] Users request transportation services using a dedicated app. The request includes information such as the departure point, destination, and desired departure time. This information is sent to the server.

[0357] Step 4:

[0358] The emotion engine analyzes the user's facial recognition data and voice input in real time to determine the user's emotional state. This data is then sent to the server.

[0359] Step 5:

[0360] The server selects the most suitable terminal and issues dispatch instructions based on the user's request and emotional state. Selection criteria include travel time, distance, and the ability to provide service tailored to the user's emotional state.

[0361] Step 6:

[0362] The terminal receives instructions and moves towards the user's location. Upon arrival, it picks up the user and boarding begins.

[0363] Step 7:

[0364] Based on instructions from the emotion engine, the device adjusts music, temperature settings, and other parameters to ensure the user's comfort as it heads towards its destination.

[0365] Step 8:

[0366] While in transit, the terminal continuously transmits real-time operational data and user status to the server, ensuring that the latest operational status is always reflected.

[0367] Step 9:

[0368] Upon arrival at the destination, the terminal allows the user to disembark and sends data about the completed trip to the server. This data is used to optimize future trips and improve services.

[0369] (Example 2)

[0370] 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".

[0371] This invention aims to provide a more comfortable and efficient transportation experience by considering the emotions of users in real time during transportation services. Conventional transportation systems have been unable to consider user emotions in demand forecasting and vehicle dispatching, leading to situations where users experience anxiety and stress. Therefore, there has been a need for new transportation services that utilize emotional information to improve user satisfaction.

[0372] 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.

[0373] In this invention, the server includes information gathering means, demand forecasting means, and placement determination means. This enables accurate demand forecasting that incorporates user sentiment information and optimal placement of transportation functions.

[0374] "Information gathering means" refers to methods for collecting various data related to transportation systems, including data on user trends and sentiment.

[0375] "Demand forecasting methods" refer to algorithms and technologies used to predict future transportation demand based on collected pedestrian flow data and sentiment information.

[0376] A "location determination means" is a means for positioning transportation functions at the optimal location, taking into account predicted transportation demand and sentiment information.

[0377] "Operation control means" refers to means for operating transportation functions automatically and efficiently, utilizing autonomous driving technology to safely transport goods to their destinations.

[0378] A "vehicle dispatch management system" is a means of selecting appropriate transportation functions based on user requests and emotional information, and dispatching transportation functions in the most optimal way for the user.

[0379] "Analysis methods" refer to technologies and methods that analyze voice data and facial recognition data obtained from users to evaluate their emotional state in real time.

[0380] This system is designed to provide efficient services in the transportation sector that take user emotions into consideration. Its main components consist of a server, terminals, users, and an emotion engine.

[0381] Server Role

[0382] The server operates on a cloud platform and uses data collection tools to gather pedestrian flow and sentiment information from users. The server utilizes AWS cloud services and uses a generative AI model based on this data to forecast demand. The demand forecasting tool processes this information and uses machine learning algorithms to predict future transportation demand. The collected data includes location information, time of day, and historical transportation data. Furthermore, the server uses a deployment decision tool to optimally allocate transportation functions, responding quickly to user requests.

[0383] Terminal role

[0384] The autonomous vehicles used as terminals utilize advanced operational control systems to safely and efficiently transport users to their destinations. Leveraging technologies such as NVIDIA DRIVE, the terminals autonomously calculate routes and optimize their operation through real-time data communication with servers. Furthermore, they are equipped with a function to adjust the in-vehicle environment according to the emotional state of the passengers. For example, playing relaxing music can reduce user stress.

[0385] User roles

[0386] Users request transportation services through a smartphone app. The app allows them to input their location, destination, and even special needs, and send this information to the server. An emotion engine analyzes the user's voice and face, acquiring emotion data in real time. This analysis is then transferred to the server as data and used to optimize the service.

[0387] The role of the emotional engine

[0388] The emotion engine uses analytical tools to analyze user voice and facial recognition data, evaluating emotional trends in real time. It utilizes AI services such as Google Cloud to precisely detect the user's emotional state. Based on this information, the server fine-tunes the transport service, providing a suitable environment and service for the user.

[0389] Examples of specific cases and prompt statements

[0390] If a user is experiencing stress during weekday morning commutes, the server, based on data from the emotion engine, instructs the device to provide a relaxing environment. As a result, users can reach their destination with reduced stress, leading to improved service satisfaction.

[0391] Example prompt: "Forecast current transportation demand and suggest which areas to deploy terminals in."

[0392] Example prompt: "Based on user sentiment data, please tell us how to optimize the service during the ride."

[0393] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0394] Step 1:

[0395] A user requests a transportation service using a smartphone app. As input, the user enters their current location, destination, and special needs into the app. The app also collects the user's current emotional state using voice and facial recognition. As output, all this information is sent to a server. In terms of specific actions, the user operates the app and enters the necessary data according to the instructions.

[0396] Step 2:

[0397] The server processes the received information using data collection tools. Input includes user information and sentiment data. A generating AI model analyzes this data to predict future transportation demand. The output generates prediction results and user sentiment information. Specifically, the server applies machine learning algorithms to compare current data with past data and analyze demand patterns.

[0398] Step 3:

[0399] The server uses demand forecasting to make appropriate placement decisions. It uses previously generated demand forecasts and sentiment information as input. This determines the optimal terminal placement, which is then sent to the terminals as instructions. Output includes placement and operational plans. Specifically, the server optimizes the terminal placement plan while considering traffic conditions and user sentiment.

[0400] Step 4:

[0401] The terminal travels along a designated route based on instructions from the server. It uses the deployment plan and operational instructions received from the server as input. The automated driving system within the terminal analyzes this data and generates real-time operational data. The operational data and the user's ride experience are sent to the server as output. Specifically, the terminal takes in GPS data and real-time traffic information to select the optimal route.

[0402] Step 5:

[0403] The emotion engine continuously monitors the user's emotional state while they are riding. It uses facial recognition data and voice data as input. This data is analyzed to detect changes in the user's emotions in real time. The emotion analysis results are sent to the server as output and used to fine-tune the service. Specifically, the emotion engine uses AI technology to evaluate the user's stress and relaxation levels while they are riding.

[0404] (Application Example 2)

[0405] 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."

[0406] Traditional transportation services focus on meeting users' physical transportation needs and lack the technology to provide services tailored to users' emotional states. Therefore, there is a need to individually optimize the transportation environment based on users' emotional states to provide more comfortable and satisfying transportation services.

[0407] 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.

[0408] In this invention, the server includes emotion analysis means for analyzing emotional states, environment setting means for adjusting the in-vehicle environment of the transportation means based on the analyzed emotional states, and prediction means for collecting passenger flow information and predicting future transportation demand. This makes it possible to provide individualized services tailored to the emotional states of users.

[0409] "Personnel flow information" refers to data about the movement of potential people using transportation services.

[0410] "Data collection means" refers to a device or method for collecting information on human flow and data on users.

[0411] A "predictive tool" is a system that estimates future transportation demand based on collected data.

[0412] "Deployment method" refers to a method of appropriately deploying transportation means in a specific area in accordance with predicted transportation demand.

[0413] "Operation control means" refers to a device or control system for automatically operating a means of transport.

[0414] A "vehicle dispatch system" is a system that selects and dispatches the most suitable mode of transportation according to the user's request.

[0415] "Emotional analysis means" refers to a system or method that analyzes a user's emotional state in real time.

[0416] "Environment setting means" refers to a device or system that adjusts the in-vehicle environment of a means of transport based on the analyzed emotional state.

[0417] This invention is a system for providing transportation services that takes into account the emotions of users and offers an efficient and comfortable environment. The system mainly consists of a server, terminals, users, and emotion analysis means.

[0418] The server acquires pedestrian flow information using data collection methods and predicts future transportation demand using machine learning algorithms based on this data. In particular, by comparing this data with past transportation data, it is possible to predict fluctuations in demand with high accuracy. Furthermore, based on the prediction results, the server optimally deploys transportation means using deployment means and efficiently operates autonomous vehicles using operation control means.

[0419] The terminal functions as an autonomous vehicle, transmitting real-time operational data to a server. It also reflects the user's emotional state, acquired through emotion analysis, in the in-vehicle environment. This process utilizes voice and facial recognition technologies; for example, using Amazon Rekognition or Google Cloud Vision enables accurate emotion detection. Through environment settings, a comfortable in-vehicle experience tailored to the user's emotional state is provided.

[0420] Users request transportation services using their smartphones, providing location information and destinations. A sentiment analysis system installed on the device analyzes the user's voice and facial expressions. This allows the user's real-time emotional state to be transmitted to the server, which then provides the most appropriate service. An example of a prompt might be, "The user wants to relax now. Please recommend some music."

[0421] For example, if a user is feeling stressed during their commute, the server can send a command to the device to play relaxing music in the car. In this way, it is possible to provide users with a more comfortable travel experience and improve the quality of transportation services.

[0422] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0423] Step 1:

[0424] The server acquires pedestrian flow information using data collection methods and stores this information in a database. The input is location data obtained from various sensors and GPS devices, and the output is an integrated pedestrian flow database. This data is used for subsequent demand forecasting.

[0425] Step 2:

[0426] The server uses machine learning algorithms to analyze human flow information in a database and predict future transportation demand. The input is historical human flow data, and the output is a predicted demand model. The data calculation here involves extracting demand patterns that take into account temporal fluctuations.

[0427] Step 3:

[0428] The server generates an appropriate transportation allocation plan based on predicted transportation demand. Based on the prediction results, it determines how many vehicles are needed in a specific area and sends that information to the transportation providers. The input is a demand model, and the output is a specific vehicle allocation plan.

[0429] Step 4:

[0430] The terminal initiates automated operation control and operates based on the deployment plan received from the server. The input is the vehicle deployment plan, and the output is real-time operation data. Position tracking and dynamic route correction of the vehicles are performed using sensors.

[0431] Step 5:

[0432] Users submit transportation requests via a smartphone app, providing location and destination information. Input is the user's request data, and output is the request data sent to the server. The user's needs are registered in their user profile.

[0433] Step 6:

[0434] The server analyzes the user's voice and facial expression data using emotion analysis tools. The input is the user's voice and image data, and the output is the analyzed emotional state. A generative AI model is applied, utilizing prompt text to identify emotions.

[0435] Step 7:

[0436] The device executes instructions to adjust the in-car environment based on the acquired emotional state. The input is the analyzed emotional state, and the output is the adjusted in-car environment data. Specific actions include music selection and temperature adjustment.

[0437] Step 8:

[0438] The server monitors the situation in real time and makes adjustments to the operation plan and in-vehicle environment as needed. Inputs are real-time operation data and in-vehicle environment data, and output is optimized service delivery. A feedback loop is formed, improving the quality of service.

[0439] 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.

[0440] 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.

[0441] 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.

[0442] [Third Embodiment]

[0443] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0444] 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.

[0445] 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).

[0446] 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.

[0447] 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.

[0448] 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).

[0449] 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.

[0450] 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.

[0451] 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.

[0452] 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.

[0453] 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.

[0454] 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".

[0455] This invention is a system for achieving efficient operation management and optimization in transportation services. This system consists of three components: a server, terminals (autonomous vehicles), and users.

[0456] Server Role

[0457] The server collects passenger flow information and uses it to predict future transportation demand. AI uses deep learning technology to learn from past data and predict future demand, determining the optimal allocation of transportation methods. Based on the generated allocation plan, the server issues movement instructions to individual terminals. Furthermore, it receives transportation requests from users, selects the most suitable terminal, and dispatches it.

[0458] Terminal role

[0459] The terminal is an autonomous vehicle that follows instructions received from the server and heads towards its destination. It transmits location and status information to the server in real time and receives instructions as needed. This enables highly automated operation and efficient transportation.

[0460] User roles

[0461] Users access the service via smartphones or dedicated terminals. When a user requests transportation, a request sent through the application allows the server to determine the user's location and destination, and a suitable vehicle is quickly dispatched. This process is simple for the user, enabling the delivery of a fast and convenient service.

[0462] Specific example

[0463] For example, consider a scenario where many people travel from a specific area to a train station during weekday morning rush hour. The server predicts this demand using historical data and real-time pedestrian flow information, and appropriately places the necessary number of terminals in that area. When a user requests a taxi, the server selects the most suitable terminal and dispatches it to the user's location. Once the terminal arrives, the user is safely and comfortably transported to their destination, the train station.

[0464] Thus, the present invention can support the efficient operation of transportation means and provide users with a high level of satisfaction.

[0465] The following describes the processing flow.

[0466] Step 1:

[0467] The server collects pedestrian flow information received from smartphones and other communication devices. This information includes location data and movement patterns.

[0468] Step 2:

[0469] The server uses AI to analyze collected pedestrian flow information and predict future transportation demand. This prediction utilizes historical trends and real-time data.

[0470] Step 3:

[0471] The server plans the optimal placement of transportation devices (terminals) based on predicted demand patterns. Specifically, it determines how many terminals should be placed in each region.

[0472] Step 4:

[0473] The server sends movement instructions to each terminal to a designated area based on the deployment plan. The terminal then begins automatic operation according to these instructions.

[0474] Step 5:

[0475] Users request taxi services using a dedicated application. The app is used to send information about the departure and destination locations to the server.

[0476] Step 6:

[0477] The server receives the user's request and selects the optimal device based on the user's location and the real-time availability of devices.

[0478] Step 7:

[0479] The server instructs the selected terminal to move towards the user's starting point. The terminal then moves in accordance with this instruction and rushes to the user's location.

[0480] Step 8:

[0481] Once the terminal arrives, the user boards it and travels to their destination. The terminal continuously receives updates from the server while en route to its destination.

[0482] Step 9:

[0483] The terminal transmits data obtained during operation (e.g., travel route, traffic conditions, etc.) to the server in real time, and the server uses this data to make future predictions.

[0484] (Example 1)

[0485] 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."

[0486] In modern transportation services, a significant mismatch between supply and demand is common, making it difficult for users to secure their reserved transportation. Efficient dispatching is particularly challenging during peak hours and in congested areas, leading to decreased operational efficiency of transportation equipment. Furthermore, there is a need for flexible systems that can respond quickly to real-time demand fluctuations.

[0487] 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.

[0488] In this invention, the server includes information gathering means, prediction means, placement determination means, automatic control means, vehicle dispatch determination means, and command transmission means. This enables efficient transportation provision by accurately predicting transportation demand and appropriately arranging transportation equipment. Furthermore, it enables real-time dispatch and operation control of transportation equipment according to the situation, thereby improving user convenience.

[0489] "Information gathering means" refers to a device or system that has the function of collecting human flow information and storing it in a database.

[0490] A "predictive means" is a device or system that has the function of predicting future transportation demand based on collected human flow information.

[0491] "Deployment determination means" refers to a device or system that has the function of efficiently deploying transportation equipment to a specific area in accordance with predicted transportation demand.

[0492] "Automatic control means" refers to a device or system that has the function of automatically operating transportation equipment to its destination.

[0493] A "vehicle dispatch determination means" is a device or system that has the function of selecting the most suitable transportation equipment in response to a transportation request from a user and executing the dispatch.

[0494] "Command transmission means" refers to a device or system that has the function of transmitting movement instructions and control commands generated by a server to transport equipment.

[0495] To implement this invention, a system is required in which three entities—a server, a terminal, and a user—work together. The server collects pedestrian flow information from each region as an information gathering means and records it in a database. This includes GPS data obtained from mobile devices and data acquired from sensors installed in the infrastructure. As an inference means, the server processes the collected data with a generative AI model to predict future transportation demand. This processing uses a deep learning framework such as TensorFlow or PyTorch.

[0496] As a concrete example, the server performs this demand forecast by providing a prompt to the AI ​​model, such as "Predict the demand for next Monday morning based on past travel patterns." Based on the forecast results, the server determines the placement of transportation equipment so that it can be used most efficiently as a means of placement determination.

[0497] The terminal receives commands from the server as an automatic control mechanism and operates the autonomous vehicle, which is a form of transportation equipment, safely and effectively. This operation is carried out while recognizing the surrounding environment using cameras and LiDAR sensors. It is also equipped with a means to transmit real-time location information to the server and receive new commands as needed.

[0498] Users access the dispatch system using a dedicated application. Through the app, they can enter their current location and destination and summon a vehicle at a convenient time. The server receives this request, selects the most suitable vehicle, and dispatches the vehicle. This allows users to travel efficiently and conveniently.

[0499] Through the specific methods described above, this invention makes it possible to realize the smooth and efficient transportation system originally envisioned.

[0500] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0501] Step 1:

[0502] The server collects real-time pedestrian flow information from various regions using data collection methods. Inputs include location data and movement history from mobile devices and infrastructure sensors. This input data is aggregated by the server and stored in a database. The output is a dataset of the latest pedestrian flow information.

[0503] Step 2:

[0504] The server uses a generative AI model as an inference tool to predict future transportation demand from collected pedestrian flow information. In this step, the dataset serves as input, and the AI ​​model is given the prompt "Predict tomorrow's transportation demand based on past movement patterns," and demand forecasting is performed through learning. The model recognizes patterns and analyzes demand fluctuations, generating predicted demand data as output.

[0505] Step 3:

[0506] The server calculates the most efficient placement of transport equipment based on predicted demand data using a placement determination mechanism. Demand data is the input, and the server uses an optimization algorithm to determine where to place the transport equipment. The output is a placement plan, which serves as the basis for sending subsequent commands.

[0507] Step 4:

[0508] The server sends movement instructions to each terminal via a command transmission device. The deployment plan is the input, and the server sends instructions to the autonomous vehicles including the destination, recommended route, and estimated arrival time. As output, each terminal receives instructions to begin movement.

[0509] Step 5:

[0510] The terminal uses automatic control mechanisms to move towards its destination based on commands. Command information from the server is input, and the terminal operates its autonomous control system to drive safely and efficiently. Using real-time data from sensors, it moves while confirming its own position and surrounding environment, and as output, it safely arrives at its destination.

[0511] Step 6:

[0512] Users utilize a vehicle dispatch system and submit transportation requests using a smartphone app. Inputs include user location information and destination data, which are sent to the server via the app. The server receives this data and provides the most suitable vehicle. Outputs include the estimated arrival time and vehicle information of the selected vehicle for the user.

[0513] Step 7:

[0514] The server selects the optimal transport vehicle using real-time data based on the vehicle dispatching decision mechanism. The user's request data and the location information of the transport vehicle are used as input, and the server performs analysis to quickly and appropriately dispatch the vehicle. As output, the selected transport vehicle moves to the user's location.

[0515] (Application Example 1)

[0516] 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."

[0517] Existing transportation services often struggle to cope with fluctuating demand during peak commuting hours, leading to stress for users. Furthermore, optimizing transportation methods in real-time to meet demand is difficult, making efficient dispatching and operation a challenge.

[0518] 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.

[0519] In this invention, the server includes information gathering means, prediction means, arrangement means, operation control means, vehicle dispatch means, input means, and display means. This enables the optimal arrangement of transportation means according to user demand and the provision of efficient routes based on destination information and desired arrival time.

[0520] "Information gathering means" refers to systems and devices that collect information on human movement and past transportation data to help forecast future demand.

[0521] A "predictive tool" is a system or device that uses machine learning algorithms to analyze collected data and predict future transportation demand.

[0522] "Deployment means" refers to systems or devices for appropriately deploying means of transport to specific areas based on predicted demand.

[0523] "Operation control means" refers to systems or devices that perform control to automatically operate means of transport.

[0524] A "vehicle dispatching system" is a system or device that selects and dispatches the most suitable means of transportation in response to a user's transportation request.

[0525] An "input method" refers to a system or device that provides an interface for users to input destination information and desired arrival time.

[0526] A "display means" refers to a system or device that displays the optimal route and estimated arrival time to the user in real time.

[0527] To realize this invention, a system is required in which three entities—a server, a terminal, and a user—work in cooperation. The server uses AWS Lambda or API Gateway to execute a program that integrates information gathering, prediction, placement, operation control, and dispatching functions. A generative AI model using TensorFlow learns from past pedestrian flow and transportation data to predict future demand. Based on this, the server determines the optimal placement of transportation and sends instructions to the terminal in real time.

[0528] The terminal is an autonomous vehicle that operates by receiving instructions from a server. It transmits location information and status data to the server in real time and optimizes the route as needed. This system enables fast and efficient transportation for users.

[0529] Users access the service using an application installed on their smartphones. They send destination information and desired arrival time to the server via input methods to receive the most suitable ride. The application is built with Flutter and displays the best route and estimated arrival time in real time.

[0530] As a concrete example, suppose a user wants to arrive at work at 8 AM. By entering the desired time in the application beforehand, the server analyzes this information and dispatches the most suitable autonomous vehicle. The route and estimated arrival time are displayed in the app in real time, ensuring a stable service experience for the user.

[0531] An example of a prompt for a generated AI model is: "I want to arrive at the office by 8:00 AM on a weekday. Based on past traffic data and pedestrian flow patterns, please suggest the best autonomous vehicle and route for me." Through this prompt, the service will be provided appropriately.

[0532] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0533] Step 1:

[0534] The server collects historical and real-time pedestrian flow data through various information gathering methods. This data is stored in a large database and used for subsequent analysis. This data collection is a crucial step that forms the basis for highly accurate demand forecasting.

[0535] Step 2:

[0536] The server uses prediction tools to forecast future transportation demand based on collected data. In this process, a generative AI model using TensorFlow analyzes the data and identifies demand patterns. The output includes peak demand times and the number of transportation options needed in a specific region.

[0537] Step 3:

[0538] The server uses deployment methods to develop a plan for deploying transportation to specific areas to meet predicted demand. This deployment plan is updated in real time, and instructions are sent from the server to each terminal. As a result, efficient deployment of transportation becomes possible.

[0539] Step 4:

[0540] The terminal automatically starts operating based on instructions sent from the server. The terminal constantly transmits its location and operating status to the server, which then uses this data to optimize the operating route as needed. This enables optimal real-time operation.

[0541] Step 5:

[0542] Users enter destination information and desired arrival time into a smartphone app using an input device. The entered information is sent to a server, which then selects the most suitable vehicle and dispatches it. This enables a very simple and efficient service for users.

[0543] Step 6:

[0544] The server transmits information through a display system to the user, showing the optimal route and estimated arrival time in real time via the application. This information is immediately reflected in the app, allowing the user to confidently head to the customer's location according to their schedule.

[0545] Step 7:

[0546] Users provide regular feedback, and the server uses this feedback to further improve the service. Using example prompts, the generated AI model can suggest transportation options that meet the user's evolving needs. Based on a prompt such as, "I want to arrive at the office by 8:00 AM on a weekday. Please suggest the best autonomous vehicle and route for me based on past traffic data and pedestrian flow patterns," the optimal transportation service will continue to be provided.

[0547] 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.

[0548] This invention is a system for achieving efficient operation and service provision in transportation services while taking user emotions into consideration. This system consists of four components: a server, a terminal (autonomous vehicle), a user, and an emotion engine.

[0549] Server Role

[0550] The server collects passenger flow information and uses AI to predict future transportation demand. Furthermore, it optimizes services based on user emotions using emotional data provided by an emotion engine. Specifically, it plans the allocation of transportation methods and manages their operation. It also receives transportation requests from users and selects and dispatches the most suitable terminal.

[0551] Terminal role

[0552] The terminal operates as an autonomous vehicle, automatically running based on instructions from the server. By transmitting real-time operational data and the user's riding status to the server, it contributes to optimizing subsequent schedules. Following instructions from an emotion engine, it can also make environmental adjustments to improve the user experience during the ride.

[0553] User roles

[0554] Users request services via smartphones or other devices, providing the server with information about their location, destination, and needs. In addition, an emotion engine analyzes the user's voice and facial expressions to detect multiple emotional states.

[0555] The role of the emotional engine

[0556] The emotion engine analyzes the user's emotions in real time using user voice data and facial recognition technology. Based on the detected emotions, the server obtains new information and adjusts the transportation service accordingly. This ensures that the user receives appropriate in-vehicle environment settings and friendly service.

[0557] Specific example

[0558] For example, if the emotion engine determines that a user is experiencing stress during their morning commute on a weekday, the server uses this information to send instructions to the terminal to optimize the music playing or the in-car temperature. As a result, the user can travel to their destination more comfortably. This process significantly improves the quality of transportation services and makes it possible to provide users with a high level of satisfaction.

[0559] The following describes the processing flow.

[0560] Step 1:

[0561] The server collects pedestrian flow information from smartphones and other communication devices, and simultaneously trains an AI model using historical transportation data. This allows it to predict future transportation demand.

[0562] Step 2:

[0563] The server determines which terminals should be placed in which areas based on predicted demand data, and sends this plan to each terminal. The terminals then move to the designated areas and prepare accordingly.

[0564] Step 3:

[0565] Users request transportation services using a dedicated app. The request includes information such as the departure point, destination, and desired departure time. This information is sent to the server.

[0566] Step 4:

[0567] The emotion engine analyzes the user's facial recognition data and voice input in real time to determine the user's emotional state. This data is then sent to the server.

[0568] Step 5:

[0569] The server selects the most suitable terminal and issues dispatch instructions based on the user's request and emotional state. Selection criteria include travel time, distance, and the ability to provide service tailored to the user's emotional state.

[0570] Step 6:

[0571] The terminal receives instructions and moves towards the user's location. Upon arrival, it picks up the user and boarding begins.

[0572] Step 7:

[0573] Based on instructions from the emotion engine, the device adjusts music, temperature settings, and other parameters to ensure the user's comfort as it heads towards its destination.

[0574] Step 8:

[0575] While in transit, the terminal continuously transmits real-time operational data and user status to the server, ensuring that the latest operational status is always reflected.

[0576] Step 9:

[0577] Upon arrival at the destination, the terminal allows the user to disembark and sends data about the completed trip to the server. This data is used to optimize future trips and improve services.

[0578] (Example 2)

[0579] 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."

[0580] This invention aims to provide a more comfortable and efficient transportation experience by considering the emotions of users in real time during transportation services. Conventional transportation systems have been unable to consider user emotions in demand forecasting and vehicle dispatching, leading to situations where users experience anxiety and stress. Therefore, there has been a need for new transportation services that utilize emotional information to improve user satisfaction.

[0581] 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.

[0582] In this invention, the server includes information gathering means, demand forecasting means, and placement determination means. This enables accurate demand forecasting that incorporates user sentiment information and optimal placement of transportation functions.

[0583] "Information gathering means" refers to methods for collecting various data related to transportation systems, including data on user trends and sentiment.

[0584] "Demand forecasting methods" refer to algorithms and technologies used to predict future transportation demand based on collected pedestrian flow data and sentiment information.

[0585] A "location determination means" is a means for positioning transportation functions at the optimal location, taking into account predicted transportation demand and sentiment information.

[0586] "Operation control means" refers to means for operating transportation functions automatically and efficiently, utilizing autonomous driving technology to safely transport goods to their destinations.

[0587] A "vehicle dispatch management system" is a means of selecting appropriate transportation functions based on user requests and emotional information, and dispatching transportation functions in the most optimal way for the user.

[0588] "Analysis methods" refer to technologies and methods that analyze voice data and facial recognition data obtained from users to evaluate their emotional state in real time.

[0589] This system is designed to provide efficient services in the transportation sector that take user emotions into consideration. Its main components consist of a server, terminals, users, and an emotion engine.

[0590] Server Role

[0591] The server operates on a cloud platform and uses data collection tools to gather pedestrian flow and sentiment information from users. The server utilizes AWS cloud services and uses a generative AI model based on this data to forecast demand. The demand forecasting tool processes this information and uses machine learning algorithms to predict future transportation demand. The collected data includes location information, time of day, and historical transportation data. Furthermore, the server uses a deployment decision tool to optimally allocate transportation functions, responding quickly to user requests.

[0592] Terminal role

[0593] The autonomous vehicles used as terminals utilize advanced operational control systems to safely and efficiently transport users to their destinations. Leveraging technologies such as NVIDIA DRIVE, the terminals autonomously calculate routes and optimize their operation through real-time data communication with servers. Furthermore, they are equipped with a function to adjust the in-vehicle environment according to the emotional state of the passengers. For example, playing relaxing music can reduce user stress.

[0594] User roles

[0595] Users request transportation services through a smartphone app. The app allows them to input their location, destination, and even special needs, and send this information to the server. An emotion engine analyzes the user's voice and face, acquiring emotion data in real time. This analysis is then transferred to the server as data and used to optimize the service.

[0596] The role of the emotional engine

[0597] The emotion engine uses analytical tools to analyze user voice and facial recognition data, evaluating emotional trends in real time. It utilizes AI services such as Google Cloud to precisely detect the user's emotional state. Based on this information, the server fine-tunes the transport service, providing a suitable environment and service for the user.

[0598] Examples of specific cases and prompt statements

[0599] If a user is experiencing stress during weekday morning commutes, the server, based on data from the emotion engine, instructs the device to provide a relaxing environment. As a result, users can reach their destination with reduced stress, leading to improved service satisfaction.

[0600] Example prompt: "Forecast current transportation demand and suggest which areas to deploy terminals in."

[0601] Example prompt: "Based on user sentiment data, please tell us how to optimize the service during the ride."

[0602] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0603] Step 1:

[0604] A user requests a transportation service using a smartphone app. As input, the user enters their current location, destination, and special needs into the app. The app also collects the user's current emotional state using voice and facial recognition. As output, all this information is sent to a server. In terms of specific actions, the user operates the app and enters the necessary data according to the instructions.

[0605] Step 2:

[0606] The server processes the received information using data collection tools. Input includes user information and sentiment data. A generating AI model analyzes this data to predict future transportation demand. The output generates prediction results and user sentiment information. Specifically, the server applies machine learning algorithms to compare current data with past data and analyze demand patterns.

[0607] Step 3:

[0608] The server uses demand forecasting to make appropriate placement decisions. It uses previously generated demand forecasts and sentiment information as input. This determines the optimal terminal placement, which is then sent to the terminals as instructions. Output includes placement and operational plans. Specifically, the server optimizes the terminal placement plan while considering traffic conditions and user sentiment.

[0609] Step 4:

[0610] The terminal travels along a designated route based on instructions from the server. It uses the deployment plan and operational instructions received from the server as input. The automated driving system within the terminal analyzes this data and generates real-time operational data. The operational data and the user's ride experience are sent to the server as output. Specifically, the terminal takes in GPS data and real-time traffic information to select the optimal route.

[0611] Step 5:

[0612] The emotion engine continuously monitors the user's emotional state while they are riding. It uses facial recognition data and voice data as input. This data is analyzed to detect changes in the user's emotions in real time. The emotion analysis results are sent to the server as output and used to fine-tune the service. Specifically, the emotion engine uses AI technology to evaluate the user's stress and relaxation levels while they are riding.

[0613] (Application Example 2)

[0614] 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."

[0615] Traditional transportation services focus on meeting users' physical transportation needs and lack the technology to provide services tailored to users' emotional states. Therefore, there is a need to individually optimize the transportation environment based on users' emotional states to provide more comfortable and satisfying transportation services.

[0616] 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.

[0617] In this invention, the server includes emotion analysis means for analyzing emotional states, environment setting means for adjusting the in-vehicle environment of the transportation means based on the analyzed emotional states, and prediction means for collecting passenger flow information and predicting future transportation demand. This makes it possible to provide individualized services tailored to the emotional states of users.

[0618] "Personnel flow information" refers to data about the movement of potential people using transportation services.

[0619] "Data collection means" refers to a device or method for collecting information on human flow and data on users.

[0620] A "predictive tool" is a system that estimates future transportation demand based on collected data.

[0621] "Deployment method" refers to a method of appropriately deploying transportation means in a specific area in accordance with predicted transportation demand.

[0622] "Operation control means" refers to a device or control system for automatically operating a means of transport.

[0623] A "vehicle dispatch system" is a system that selects and dispatches the most suitable mode of transportation according to the user's request.

[0624] "Emotional analysis means" refers to a system or method that analyzes a user's emotional state in real time.

[0625] "Environment setting means" refers to a device or system that adjusts the in-vehicle environment of a means of transport based on the analyzed emotional state.

[0626] This invention is a system for providing transportation services that takes into account the emotions of users and offers an efficient and comfortable environment. The system mainly consists of a server, terminals, users, and emotion analysis means.

[0627] The server acquires pedestrian flow information using data collection methods and predicts future transportation demand using machine learning algorithms based on this data. In particular, by comparing this data with past transportation data, it is possible to predict fluctuations in demand with high accuracy. Furthermore, based on the prediction results, the server optimally deploys transportation means using deployment means and efficiently operates autonomous vehicles using operation control means.

[0628] The terminal functions as an autonomous vehicle, transmitting real-time operational data to a server. It also reflects the user's emotional state, acquired through emotion analysis, in the in-vehicle environment. This process utilizes voice and facial recognition technologies; for example, using Amazon Rekognition or Google Cloud Vision enables accurate emotion detection. Through environment settings, a comfortable in-vehicle experience tailored to the user's emotional state is provided.

[0629] Users request transportation services using their smartphones, providing location information and destinations. A sentiment analysis system installed on the device analyzes the user's voice and facial expressions. This allows the user's real-time emotional state to be transmitted to the server, which then provides the most appropriate service. An example of a prompt might be, "The user wants to relax now. Please recommend some music."

[0630] For example, if a user is feeling stressed during their commute, the server can send a command to the device to play relaxing music in the car. In this way, it is possible to provide users with a more comfortable travel experience and improve the quality of transportation services.

[0631] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0632] Step 1:

[0633] The server acquires pedestrian flow information using data collection methods and stores this information in a database. The input is location data obtained from various sensors and GPS devices, and the output is an integrated pedestrian flow database. This data is used for subsequent demand forecasting.

[0634] Step 2:

[0635] The server uses machine learning algorithms to analyze human flow information in a database and predict future transportation demand. The input is historical human flow data, and the output is a predicted demand model. The data calculation here involves extracting demand patterns that take into account temporal fluctuations.

[0636] Step 3:

[0637] The server generates an appropriate transportation allocation plan based on predicted transportation demand. Based on the prediction results, it determines how many vehicles are needed in a specific area and sends that information to the transportation providers. The input is a demand model, and the output is a specific vehicle allocation plan.

[0638] Step 4:

[0639] The terminal initiates automated operation control and operates based on the deployment plan received from the server. The input is the vehicle deployment plan, and the output is real-time operation data. Position tracking and dynamic route correction of the vehicles are performed using sensors.

[0640] Step 5:

[0641] Users submit transportation requests via a smartphone app, providing location and destination information. Input is the user's request data, and output is the request data sent to the server. The user's needs are registered in their user profile.

[0642] Step 6:

[0643] The server analyzes the user's voice and facial expression data using emotion analysis tools. The input is the user's voice and image data, and the output is the analyzed emotional state. A generative AI model is applied, utilizing prompt text to identify emotions.

[0644] Step 7:

[0645] The device executes instructions to adjust the in-car environment based on the acquired emotional state. The input is the analyzed emotional state, and the output is the adjusted in-car environment data. Specific actions include music selection and temperature adjustment.

[0646] Step 8:

[0647] The server monitors the situation in real time and makes adjustments to the operation plan and in-vehicle environment as needed. Inputs are real-time operation data and in-vehicle environment data, and output is optimized service delivery. A feedback loop is formed, improving the quality of service.

[0648] 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.

[0649] 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.

[0650] 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.

[0651] [Fourth Embodiment]

[0652] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0653] 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.

[0654] 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).

[0655] 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.

[0656] 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.

[0657] 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).

[0658] 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.

[0659] 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.

[0660] 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.

[0661] 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.

[0662] 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.

[0663] 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.

[0664] 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".

[0665] This invention is a system for achieving efficient operation management and optimization in transportation services. This system consists of three components: a server, terminals (autonomous vehicles), and users.

[0666] Server Role

[0667] The server collects passenger flow information and uses it to predict future transportation demand. AI uses deep learning technology to learn from past data and predict future demand, determining the optimal allocation of transportation methods. Based on the generated allocation plan, the server issues movement instructions to individual terminals. Furthermore, it receives transportation requests from users, selects the most suitable terminal, and dispatches it.

[0668] Terminal role

[0669] The terminal is an autonomous vehicle that follows instructions received from the server and heads towards its destination. It transmits location and status information to the server in real time and receives instructions as needed. This enables highly automated operation and efficient transportation.

[0670] User roles

[0671] Users access the service via smartphones or dedicated terminals. When a user requests transportation, a request sent through the application allows the server to determine the user's location and destination, and a suitable vehicle is quickly dispatched. This process is simple for the user, enabling the delivery of a fast and convenient service.

[0672] Specific example

[0673] For example, consider a scenario where many people travel from a specific area to a train station during weekday morning rush hour. The server predicts this demand using historical data and real-time pedestrian flow information, and appropriately places the necessary number of terminals in that area. When a user requests a taxi, the server selects the most suitable terminal and dispatches it to the user's location. Once the terminal arrives, the user is safely and comfortably transported to their destination, the train station.

[0674] Thus, the present invention can support the efficient operation of transportation means and provide users with a high level of satisfaction.

[0675] The following describes the processing flow.

[0676] Step 1:

[0677] The server collects pedestrian flow information received from smartphones and other communication devices. This information includes location data and movement patterns.

[0678] Step 2:

[0679] The server uses AI to analyze collected pedestrian flow information and predict future transportation demand. This prediction utilizes historical trends and real-time data.

[0680] Step 3:

[0681] The server plans the optimal placement of transportation devices (terminals) based on predicted demand patterns. Specifically, it determines how many terminals should be placed in each region.

[0682] Step 4:

[0683] The server sends movement instructions to each terminal to a designated area based on the deployment plan. The terminal then begins automatic operation according to these instructions.

[0684] Step 5:

[0685] Users request taxi services using a dedicated application. The app is used to send information about the departure and destination locations to the server.

[0686] Step 6:

[0687] The server receives the user's request and selects the optimal device based on the user's location and the real-time availability of devices.

[0688] Step 7:

[0689] The server instructs the selected terminal to move towards the user's starting point. The terminal then moves in accordance with this instruction and rushes to the user's location.

[0690] Step 8:

[0691] Once the terminal arrives, the user boards it and travels to their destination. The terminal continuously receives updates from the server while en route to its destination.

[0692] Step 9:

[0693] The terminal transmits data obtained during operation (e.g., travel route, traffic conditions, etc.) to the server in real time, and the server uses this data to make future predictions.

[0694] (Example 1)

[0695] 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".

[0696] In modern transportation services, a significant mismatch between supply and demand is common, making it difficult for users to secure their reserved transportation. Efficient dispatching is particularly challenging during peak hours and in congested areas, leading to decreased operational efficiency of transportation equipment. Furthermore, there is a need for flexible systems that can respond quickly to real-time demand fluctuations.

[0697] 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.

[0698] In this invention, the server includes information gathering means, prediction means, placement determination means, automatic control means, vehicle dispatch determination means, and command transmission means. This enables efficient transportation provision by accurately predicting transportation demand and appropriately arranging transportation equipment. Furthermore, it enables real-time dispatch and operation control of transportation equipment according to the situation, thereby improving user convenience.

[0699] "Information gathering means" refers to a device or system that has the function of collecting human flow information and storing it in a database.

[0700] A "predictive means" is a device or system that has the function of predicting future transportation demand based on collected human flow information.

[0701] "Deployment determination means" refers to a device or system that has the function of efficiently deploying transportation equipment to a specific area in accordance with predicted transportation demand.

[0702] "Automatic control means" refers to a device or system that has the function of automatically operating transportation equipment to its destination.

[0703] A "vehicle dispatch determination means" is a device or system that has the function of selecting the most suitable transportation equipment in response to a transportation request from a user and executing the dispatch.

[0704] "Command transmission means" refers to a device or system that has the function of transmitting movement instructions and control commands generated by a server to transport equipment.

[0705] To implement this invention, a system is required in which three entities—a server, a terminal, and a user—work together. The server collects pedestrian flow information from each region as an information gathering means and records it in a database. This includes GPS data obtained from mobile devices and data acquired from sensors installed in the infrastructure. As an inference means, the server processes the collected data with a generative AI model to predict future transportation demand. This processing uses a deep learning framework such as TensorFlow or PyTorch.

[0706] As a concrete example, the server performs this demand forecast by providing a prompt to the AI ​​model, such as "Predict the demand for next Monday morning based on past travel patterns." Based on the forecast results, the server determines the placement of transportation equipment so that it can be used most efficiently as a means of placement determination.

[0707] The terminal receives commands from the server as an automatic control mechanism and operates the autonomous vehicle, which is a form of transportation equipment, safely and effectively. This operation is carried out while recognizing the surrounding environment using cameras and LiDAR sensors. It is also equipped with a means to transmit real-time location information to the server and receive new commands as needed.

[0708] Users access the dispatch system using a dedicated application. Through the app, they can enter their current location and destination and summon a vehicle at a convenient time. The server receives this request, selects the most suitable vehicle, and dispatches the vehicle. This allows users to travel efficiently and conveniently.

[0709] Through the specific methods described above, this invention makes it possible to realize the smooth and efficient transportation system originally envisioned.

[0710] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0711] Step 1:

[0712] The server collects real-time pedestrian flow information from various regions using data collection methods. Inputs include location data and movement history from mobile devices and infrastructure sensors. This input data is aggregated by the server and stored in a database. The output is a dataset of the latest pedestrian flow information.

[0713] Step 2:

[0714] The server uses a generative AI model as an inference tool to predict future transportation demand from collected pedestrian flow information. In this step, the dataset serves as input, and the AI ​​model is given the prompt "Predict tomorrow's transportation demand based on past movement patterns," and demand forecasting is performed through learning. The model recognizes patterns and analyzes demand fluctuations, generating predicted demand data as output.

[0715] Step 3:

[0716] The server calculates the most efficient placement of transport equipment based on predicted demand data using a placement determination mechanism. Demand data is the input, and the server uses an optimization algorithm to determine where to place the transport equipment. The output is a placement plan, which serves as the basis for sending subsequent commands.

[0717] Step 4:

[0718] The server sends movement instructions to each terminal via a command transmission device. The deployment plan is the input, and the server sends instructions to the autonomous vehicles including the destination, recommended route, and estimated arrival time. As output, each terminal receives instructions to begin movement.

[0719] Step 5:

[0720] The terminal uses automatic control mechanisms to move towards its destination based on commands. Command information from the server is input, and the terminal operates its autonomous control system to drive safely and efficiently. Using real-time data from sensors, it moves while confirming its own position and surrounding environment, and as output, it safely arrives at its destination.

[0721] Step 6:

[0722] Users utilize a vehicle dispatch system and submit transportation requests using a smartphone app. Inputs include user location information and destination data, which are sent to the server via the app. The server receives this data and provides the most suitable vehicle. Outputs include the estimated arrival time and vehicle information of the selected vehicle for the user.

[0723] Step 7:

[0724] The server selects the optimal transport vehicle using real-time data based on the vehicle dispatching decision mechanism. The user's request data and the location information of the transport vehicle are used as input, and the server performs analysis to quickly and appropriately dispatch the vehicle. As output, the selected transport vehicle moves to the user's location.

[0725] (Application Example 1)

[0726] 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".

[0727] Existing transportation services often struggle to cope with fluctuating demand during peak commuting hours, leading to stress for users. Furthermore, optimizing transportation methods in real-time to meet demand is difficult, making efficient dispatching and operation a challenge.

[0728] 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.

[0729] In this invention, the server includes information gathering means, prediction means, arrangement means, operation control means, vehicle dispatch means, input means, and display means. This enables the optimal arrangement of transportation means according to user demand and the provision of efficient routes based on destination information and desired arrival time.

[0730] "Information gathering means" refers to systems and devices that collect information on human movement and past transportation data to help forecast future demand.

[0731] A "predictive tool" is a system or device that uses machine learning algorithms to analyze collected data and predict future transportation demand.

[0732] "Deployment means" refers to systems or devices for appropriately deploying means of transport to specific areas based on predicted demand.

[0733] "Operation control means" refers to systems or devices that perform control to automatically operate means of transport.

[0734] A "vehicle dispatching system" is a system or device that selects and dispatches the most suitable means of transportation in response to a user's transportation request.

[0735] An "input method" refers to a system or device that provides an interface for users to input destination information and desired arrival time.

[0736] A "display means" refers to a system or device that displays the optimal route and estimated arrival time to the user in real time.

[0737] To realize this invention, a system is required in which three entities—a server, a terminal, and a user—work in cooperation. The server uses AWS Lambda or API Gateway to execute a program that integrates information gathering, prediction, placement, operation control, and dispatching functions. A generative AI model using TensorFlow learns from past pedestrian flow and transportation data to predict future demand. Based on this, the server determines the optimal placement of transportation and sends instructions to the terminal in real time.

[0738] The terminal is an autonomous vehicle that operates by receiving instructions from a server. It transmits location information and status data to the server in real time and optimizes the route as needed. This system enables fast and efficient transportation for users.

[0739] Users access the service using an application installed on their smartphones. They send destination information and desired arrival time to the server via input methods to receive the most suitable ride. The application is built with Flutter and displays the best route and estimated arrival time in real time.

[0740] As a concrete example, suppose a user wants to arrive at work at 8 AM. By entering the desired time in the application beforehand, the server analyzes this information and dispatches the most suitable autonomous vehicle. The route and estimated arrival time are displayed in the app in real time, ensuring a stable service experience for the user.

[0741] An example of a prompt for a generated AI model is: "I want to arrive at the office by 8:00 AM on a weekday. Based on past traffic data and pedestrian flow patterns, please suggest the best autonomous vehicle and route for me." Through this prompt, the service will be provided appropriately.

[0742] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0743] Step 1:

[0744] The server collects historical and real-time pedestrian flow data through various information gathering methods. This data is stored in a large database and used for subsequent analysis. This data collection is a crucial step that forms the basis for highly accurate demand forecasting.

[0745] Step 2:

[0746] The server uses prediction tools to forecast future transportation demand based on collected data. In this process, a generative AI model using TensorFlow analyzes the data and identifies demand patterns. The output includes peak demand times and the number of transportation options needed in a specific region.

[0747] Step 3:

[0748] The server uses deployment methods to develop a plan for deploying transportation to specific areas to meet predicted demand. This deployment plan is updated in real time, and instructions are sent from the server to each terminal. As a result, efficient deployment of transportation becomes possible.

[0749] Step 4:

[0750] The terminal automatically starts operating based on instructions sent from the server. The terminal constantly transmits its location and operating status to the server, which then uses this data to optimize the operating route as needed. This enables optimal real-time operation.

[0751] Step 5:

[0752] Users enter destination information and desired arrival time into a smartphone app using an input device. The entered information is sent to a server, which then selects the most suitable vehicle and dispatches it. This enables a very simple and efficient service for users.

[0753] Step 6:

[0754] The server transmits information through a display system to the user, showing the optimal route and estimated arrival time in real time via the application. This information is immediately reflected in the app, allowing the user to confidently head to the customer's location according to their schedule.

[0755] Step 7:

[0756] Users provide regular feedback, and the server uses this feedback to further improve the service. Using example prompts, the generated AI model can suggest transportation options that meet the user's evolving needs. Based on a prompt such as, "I want to arrive at the office by 8:00 AM on a weekday. Please suggest the best autonomous vehicle and route for me based on past traffic data and pedestrian flow patterns," the optimal transportation service will continue to be provided.

[0757] 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.

[0758] This invention is a system for achieving efficient operation and service provision in transportation services while taking user emotions into consideration. This system consists of four components: a server, a terminal (autonomous vehicle), a user, and an emotion engine.

[0759] Server Role

[0760] The server collects passenger flow information and uses AI to predict future transportation demand. Furthermore, it optimizes services based on user emotions using emotional data provided by an emotion engine. Specifically, it plans the allocation of transportation methods and manages their operation. It also receives transportation requests from users and selects and dispatches the most suitable terminal.

[0761] Terminal role

[0762] The terminal operates as an autonomous vehicle, automatically running based on instructions from the server. By transmitting real-time operational data and the user's riding status to the server, it contributes to optimizing subsequent schedules. Following instructions from an emotion engine, it can also make environmental adjustments to improve the user experience during the ride.

[0763] User roles

[0764] Users request services via smartphones or other devices, providing the server with information about their location, destination, and needs. In addition, an emotion engine analyzes the user's voice and facial expressions to detect multiple emotional states.

[0765] The role of the emotional engine

[0766] The emotion engine analyzes the user's emotions in real time using user voice data and facial recognition technology. Based on the detected emotions, the server obtains new information and adjusts the transportation service accordingly. This ensures that the user receives appropriate in-vehicle environment settings and friendly service.

[0767] Specific example

[0768] For example, if the emotion engine determines that a user is experiencing stress during their morning commute on a weekday, the server uses this information to send instructions to the terminal to optimize the music playing or the in-car temperature. As a result, the user can travel to their destination more comfortably. This process significantly improves the quality of transportation services and makes it possible to provide users with a high level of satisfaction.

[0769] The following describes the processing flow.

[0770] Step 1:

[0771] The server collects pedestrian flow information from smartphones and other communication devices, and simultaneously trains an AI model using historical transportation data. This allows it to predict future transportation demand.

[0772] Step 2:

[0773] The server determines which terminals should be placed in which areas based on predicted demand data, and sends this plan to each terminal. The terminals then move to the designated areas and prepare accordingly.

[0774] Step 3:

[0775] Users request transportation services using a dedicated app. The request includes information such as the departure point, destination, and desired departure time. This information is sent to the server.

[0776] Step 4:

[0777] The emotion engine analyzes the user's facial recognition data and voice input in real time to determine the user's emotional state. This data is then sent to the server.

[0778] Step 5:

[0779] The server selects the most suitable terminal and issues dispatch instructions based on the user's request and emotional state. Selection criteria include travel time, distance, and the ability to provide service tailored to the user's emotional state.

[0780] Step 6:

[0781] The terminal receives instructions and moves towards the user's location. Upon arrival, it picks up the user and boarding begins.

[0782] Step 7:

[0783] Based on instructions from the emotion engine, the device adjusts music, temperature settings, and other parameters to ensure the user's comfort as it heads towards its destination.

[0784] Step 8:

[0785] While in transit, the terminal continuously transmits real-time operational data and user status to the server, ensuring that the latest operational status is always reflected.

[0786] Step 9:

[0787] Upon arrival at the destination, the terminal allows the user to disembark and sends data about the completed trip to the server. This data is used to optimize future trips and improve services.

[0788] (Example 2)

[0789] 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".

[0790] This invention aims to provide a more comfortable and efficient transportation experience by considering the emotions of users in real time during transportation services. Conventional transportation systems have been unable to consider user emotions in demand forecasting and vehicle dispatching, leading to situations where users experience anxiety and stress. Therefore, there has been a need for new transportation services that utilize emotional information to improve user satisfaction.

[0791] 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.

[0792] In this invention, the server includes information gathering means, demand forecasting means, and placement determination means. This enables accurate demand forecasting that incorporates user sentiment information and optimal placement of transportation functions.

[0793] "Information gathering means" refers to methods for collecting various data related to transportation systems, including data on user trends and sentiment.

[0794] "Demand forecasting methods" refer to algorithms and technologies used to predict future transportation demand based on collected pedestrian flow data and sentiment information.

[0795] A "location determination means" is a means for positioning transportation functions at the optimal location, taking into account predicted transportation demand and sentiment information.

[0796] "Operation control means" refers to means for operating transportation functions automatically and efficiently, utilizing autonomous driving technology to safely transport goods to their destinations.

[0797] A "vehicle dispatch management system" is a means of selecting appropriate transportation functions based on user requests and emotional information, and dispatching transportation functions in the most optimal way for the user.

[0798] "Analysis methods" refer to technologies and methods that analyze voice data and facial recognition data obtained from users to evaluate their emotional state in real time.

[0799] This system is designed to provide efficient services in the transportation sector that take user emotions into consideration. Its main components consist of a server, terminals, users, and an emotion engine.

[0800] Server Role

[0801] The server operates on a cloud platform and uses data collection tools to gather pedestrian flow and sentiment information from users. The server utilizes AWS cloud services and uses a generative AI model based on this data to forecast demand. The demand forecasting tool processes this information and uses machine learning algorithms to predict future transportation demand. The collected data includes location information, time of day, and historical transportation data. Furthermore, the server uses a deployment decision tool to optimally allocate transportation functions, responding quickly to user requests.

[0802] Terminal role

[0803] The autonomous vehicles used as terminals utilize advanced operational control systems to safely and efficiently transport users to their destinations. Leveraging technologies such as NVIDIA DRIVE, the terminals autonomously calculate routes and optimize their operation through real-time data communication with servers. Furthermore, they are equipped with a function to adjust the in-vehicle environment according to the emotional state of the passengers. For example, playing relaxing music can reduce user stress.

[0804] User roles

[0805] Users request transportation services through a smartphone app. The app allows them to input their location, destination, and even special needs, and send this information to the server. An emotion engine analyzes the user's voice and face, acquiring emotion data in real time. This analysis is then transferred to the server as data and used to optimize the service.

[0806] The role of the emotional engine

[0807] The emotion engine uses analytical tools to analyze user voice and facial recognition data, evaluating emotional trends in real time. It utilizes AI services such as Google Cloud to precisely detect the user's emotional state. Based on this information, the server fine-tunes the transport service, providing a suitable environment and service for the user.

[0808] Examples of specific cases and prompt statements

[0809] If a user is experiencing stress during weekday morning commutes, the server, based on data from the emotion engine, instructs the device to provide a relaxing environment. As a result, users can reach their destination with reduced stress, leading to improved service satisfaction.

[0810] Example prompt: "Forecast current transportation demand and suggest which areas to deploy terminals in."

[0811] Example prompt: "Based on user sentiment data, please tell us how to optimize the service during the ride."

[0812] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0813] Step 1:

[0814] A user requests a transportation service using a smartphone app. As input, the user enters their current location, destination, and special needs into the app. The app also collects the user's current emotional state using voice and facial recognition. As output, all this information is sent to a server. In terms of specific actions, the user operates the app and enters the necessary data according to the instructions.

[0815] Step 2:

[0816] The server processes the received information using data collection tools. Input includes user information and sentiment data. A generating AI model analyzes this data to predict future transportation demand. The output generates prediction results and user sentiment information. Specifically, the server applies machine learning algorithms to compare current data with past data and analyze demand patterns.

[0817] Step 3:

[0818] The server uses demand forecasting to make appropriate placement decisions. It uses previously generated demand forecasts and sentiment information as input. This determines the optimal terminal placement, which is then sent to the terminals as instructions. Output includes placement and operational plans. Specifically, the server optimizes the terminal placement plan while considering traffic conditions and user sentiment.

[0819] Step 4:

[0820] The terminal travels along a designated route based on instructions from the server. It uses the deployment plan and operational instructions received from the server as input. The automated driving system within the terminal analyzes this data and generates real-time operational data. The operational data and the user's ride experience are sent to the server as output. Specifically, the terminal takes in GPS data and real-time traffic information to select the optimal route.

[0821] Step 5:

[0822] The emotion engine continuously monitors the user's emotional state while they are riding. It uses facial recognition data and voice data as input. This data is analyzed to detect changes in the user's emotions in real time. The emotion analysis results are sent to the server as output and used to fine-tune the service. Specifically, the emotion engine uses AI technology to evaluate the user's stress and relaxation levels while they are riding.

[0823] (Application Example 2)

[0824] 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".

[0825] Traditional transportation services focus on meeting users' physical transportation needs and lack the technology to provide services tailored to users' emotional states. Therefore, there is a need to individually optimize the transportation environment based on users' emotional states to provide more comfortable and satisfying transportation services.

[0826] 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.

[0827] In this invention, the server includes emotion analysis means for analyzing emotional states, environment setting means for adjusting the in-vehicle environment of the transportation means based on the analyzed emotional states, and prediction means for collecting passenger flow information and predicting future transportation demand. This makes it possible to provide individualized services tailored to the emotional states of users.

[0828] "Personnel flow information" refers to data about the movement of potential people using transportation services.

[0829] "Data collection means" refers to a device or method for collecting information on human flow and data on users.

[0830] A "predictive tool" is a system that estimates future transportation demand based on collected data.

[0831] "Deployment method" refers to a method of appropriately deploying transportation means in a specific area in accordance with predicted transportation demand.

[0832] "Operation control means" refers to a device or control system for automatically operating a means of transport.

[0833] A "vehicle dispatch system" is a system that selects and dispatches the most suitable mode of transportation according to the user's request.

[0834] "Emotional analysis means" refers to a system or method that analyzes a user's emotional state in real time.

[0835] "Environment setting means" refers to a device or system that adjusts the in-vehicle environment of a means of transport based on the analyzed emotional state.

[0836] This invention is a system for providing transportation services that takes into account the emotions of users and offers an efficient and comfortable environment. The system mainly consists of a server, terminals, users, and emotion analysis means.

[0837] The server acquires pedestrian flow information using data collection methods and predicts future transportation demand using machine learning algorithms based on this data. In particular, by comparing this data with past transportation data, it is possible to predict fluctuations in demand with high accuracy. Furthermore, based on the prediction results, the server optimally deploys transportation means using deployment means and efficiently operates autonomous vehicles using operation control means.

[0838] The terminal functions as an autonomous vehicle, transmitting real-time operational data to a server. It also reflects the user's emotional state, acquired through emotion analysis, in the in-vehicle environment. This process utilizes voice and facial recognition technologies; for example, using Amazon Rekognition or Google Cloud Vision enables accurate emotion detection. Through environment settings, a comfortable in-vehicle experience tailored to the user's emotional state is provided.

[0839] Users request transportation services using their smartphones, providing location information and destinations. A sentiment analysis system installed on the device analyzes the user's voice and facial expressions. This allows the user's real-time emotional state to be transmitted to the server, which then provides the most appropriate service. An example of a prompt might be, "The user wants to relax now. Please recommend some music."

[0840] For example, if a user is feeling stressed during their commute, the server can send a command to the device to play relaxing music in the car. In this way, it is possible to provide users with a more comfortable travel experience and improve the quality of transportation services.

[0841] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0842] Step 1:

[0843] The server acquires pedestrian flow information using data collection methods and stores this information in a database. The input is location data obtained from various sensors and GPS devices, and the output is an integrated pedestrian flow database. This data is used for subsequent demand forecasting.

[0844] Step 2:

[0845] The server uses machine learning algorithms to analyze human flow information in a database and predict future transportation demand. The input is historical human flow data, and the output is a predicted demand model. The data calculation here involves extracting demand patterns that take into account temporal fluctuations.

[0846] Step 3:

[0847] The server generates an appropriate transportation allocation plan based on predicted transportation demand. Based on the prediction results, it determines how many vehicles are needed in a specific area and sends that information to the transportation providers. The input is a demand model, and the output is a specific vehicle allocation plan.

[0848] Step 4:

[0849] The terminal initiates automated operation control and operates based on the deployment plan received from the server. The input is the vehicle deployment plan, and the output is real-time operation data. Position tracking and dynamic route correction of the vehicles are performed using sensors.

[0850] Step 5:

[0851] Users submit transportation requests via a smartphone app, providing location and destination information. Input is the user's request data, and output is the request data sent to the server. The user's needs are registered in their user profile.

[0852] Step 6:

[0853] The server analyzes the user's voice and facial expression data using emotion analysis tools. The input is the user's voice and image data, and the output is the analyzed emotional state. A generative AI model is applied, utilizing prompt text to identify emotions.

[0854] Step 7:

[0855] The device executes instructions to adjust the in-car environment based on the acquired emotional state. The input is the analyzed emotional state, and the output is the adjusted in-car environment data. Specific actions include music selection and temperature adjustment.

[0856] Step 8:

[0857] The server monitors the situation in real time and makes adjustments to the operation plan and in-vehicle environment as needed. Inputs are real-time operation data and in-vehicle environment data, and output is optimized service delivery. A feedback loop is formed, improving the quality of service.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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."

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] 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.

[0877] 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.

[0878] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0879] The following is further disclosed regarding the embodiments described above.

[0880] (Claim 1)

[0881] A data collection method for collecting pedestrian flow information,

[0882] A forecasting method that predicts future transportation demand based on collected human flow information,

[0883] A means of appropriately arranging transportation means in a specific area based on predicted transportation demand,

[0884] A means of operation control that automatically operates the means of transport,

[0885] A dispatch system that receives transportation requests from users, selects and dispatches transportation methods according to those requests,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, characterized in that the prediction means predicts future transportation demand by comparing it with past transportation data using a machine learning algorithm.

[0889] (Claim 3)

[0890] The system according to claim 1, characterized in that the dispatching means selects the optimal means of transport using the user's location information and real-time information on means of transport.

[0891] "Example 1"

[0892] (Claim 1)

[0893] Information gathering means for collecting information on human movement,

[0894] A method for predicting future transportation demand based on collected human flow information,

[0895] A means for determining the placement of transportation equipment in a specific area based on predicted transportation demand,

[0896] Automatic control means for automatically operating transportation equipment,

[0897] A dispatch determination means that receives transportation requests from users and selects and dispatches the most suitable transportation equipment in response to those requests,

[0898] A command transmission means that monitors the location information of transport equipment in real time and generates and transmits movement instructions,

[0899] A system that includes this.

[0900] (Claim 2)

[0901] The system according to claim 1, characterized in that the estimation means predicts future transportation demand by matching it with past travel pattern data using a machine learning algorithm.

[0902] (Claim 3)

[0903] The system according to claim 1, characterized in that the dispatch determination means selects the optimal transport equipment using the user's location information and real-time information on transport equipment, and further enables a rapid response to user requests.

[0904] "Application Example 1"

[0905] (Claim 1)

[0906] Information gathering means for collecting information on human movement,

[0907] A forecasting method that predicts future transportation demand based on collected human flow information,

[0908] A means of appropriately arranging transportation means in a specific area based on predicted transportation demand,

[0909] A means of operation control that automatically operates the means of transport,

[0910] A dispatch system that receives transportation requests from users, selects and dispatches transportation methods according to those requests,

[0911] An input means that provides an interface in which the user can input destination information and desired arrival time,

[0912] A display means that shows the optimal route and estimated arrival time in real time,

[0913] A system that includes this.

[0914] (Claim 2)

[0915] The system according to claim 1, characterized in that the prediction means uses a machine learning algorithm to compare with past transportation data and performs demand forecasting by combining multiple prediction elements.

[0916] (Claim 3)

[0917] The system according to claim 1, characterized in that the dispatching means selects the optimal mode of transport using the user's location information and real-time information on the mode of transport, and further calculates the optimal route based on destination information and desired arrival time.

[0918] "Example 2 of combining an emotion engine"

[0919] (Claim 1)

[0920] Information gathering means for collecting information on human movement,

[0921] A demand forecasting method that predicts future transportation demand based on collected pedestrian flow information and user sentiment information,

[0922] A means for determining the appropriate placement of transportation functions in a specific area based on predicted transportation demand and sentiment information,

[0923] Operation control means for operating the transportation function automatically and independently,

[0924] A dispatch management system that receives transportation requests from users, selects the most suitable transportation function according to the transportation request and emotional information, and dispatches vehicles accordingly.

[0925] An analytical method for evaluating emotional state by analyzing user voice data and facial recognition data,

[0926] A system that includes this.

[0927] (Claim 2)

[0928] The system according to claim 1, characterized in that the demand forecasting means uses a machine learning algorithm to compare past transportation data and collected sentiment information to predict future transportation demand.

[0929] (Claim 3)

[0930] The system according to claim 1, characterized in that the dispatch management means selects the optimal transportation function using the user's location information, real-time information on transportation functions, and emotional state.

[0931] "Application example 2 when combining with an emotional engine"

[0932] (Claim 1)

[0933] A data collection method for collecting pedestrian flow information,

[0934] A forecasting method that predicts future transportation demand based on collected human flow information,

[0935] A means of appropriately arranging transportation means in a specific area based on predicted transportation demand,

[0936] A means of operation control that automatically operates the means of transport,

[0937] A dispatch system that receives transportation requests from users, selects and dispatches transportation methods according to those requests,

[0938] A means of analyzing the emotional state of users,

[0939] An environment setting means that adjusts the in-vehicle environment of a means of transport based on the analyzed emotional state,

[0940] A system that includes this.

[0941] (Claim 2)

[0942] The system according to claim 1, characterized in that the prediction means predicts future transportation demand by comparing it with past transportation data using a machine learning algorithm.

[0943] (Claim 3)

[0944] The system according to claim 1, characterized in that the dispatching means selects the optimal means of transport using the user's location information and real-time information on means of transport. [Explanation of symbols]

[0945] 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. Information gathering means for collecting information on human movement, A forecasting method that predicts future transportation demand based on collected human flow information, A means of appropriately arranging transportation means in a specific area based on predicted transportation demand, A means of operation control that automatically operates the means of transport, A dispatch system that receives transportation requests from users, selects and dispatches transportation methods according to those requests, An input means that provides an interface in which the user can input destination information and desired arrival time, A display means that shows the optimal route and estimated arrival time in real time, A system that includes this.

2. The system according to claim 1, characterized in that the prediction means uses a machine learning algorithm to compare with past transportation data and performs demand forecasting by combining multiple prediction elements.

3. The system according to claim 1, characterized in that the dispatching means selects the optimal mode of transport using the user's location information and real-time information on the mode of transport, and further calculates the optimal route based on destination information and desired arrival time.

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