system

Abstract

We provide the system. [Solution] A means for collecting event information and weather information, Means for collecting event information and weather information, A means for predicting the locations and times when people will gather by analyzing the aforementioned event information and weather information, and for predicting the demand for transportation methods, means for generating dispatch instructions for autonomous vehicles based on the aforementioned prediction results, Means for notifying the control system of the autonomous vehicle of the dispatch instruction, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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] At the end of an event or in the face of sudden weather changes, it has been difficult for conventional transportation means to accurately predict demand and efficiently dispatch vehicles. As a result, there are problems such as wasted operation of transportation means, waiting time for passengers, deterioration of service quality, and increase in environmental load.

Means for Solving the Problems

[0005] In order to solve the above problems, the present invention includes means for collecting event information and weather information, and means for analyzing these information to predict places and times where people gather. Furthermore, based on the prediction results, a vehicle dispatch instruction for transportation means is generated and notified to an operator, thereby providing a system that realizes efficient vehicle dispatch.

[0006] "Event information" refers to detailed information about the date, time, location, and content of a publicly announced event.

[0007] "Weather information" refers to data related to weather forecasts for a specific region, including information such as precipitation, temperature, and wind speed.

[0008] "Analysis" refers to the process of processing and analyzing collected data to derive useful conclusions or predictions.

[0009] "Transportation" refers to vehicles and related operating systems used to move goods or people within a specific area.

[0010] "Dispatch instructions" refer to specific location and time instructions necessary for the efficient operation of transportation.

[0011] "Operator" refers to a person or organization responsible for managing transportation and executing dispatch instructions. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7]It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the language used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include 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.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention provides a system that predicts the flow of people necessary for the effective operation of transportation and dispatches appropriate vehicles based on that prediction. The system consists of a server, terminals, and users.

[0034] Server role:

[0035] The server collects event and weather information from the internet. For example, it obtains concert schedules for the area and the probability of precipitation for the day from weather forecast websites. Using this information, an AI model analyzes it to predict the times and places where people are likely to gather. Based on the analysis results, it predicts that demand will increase at specific locations and times and generates dispatch instructions accordingly.

[0036] Terminal role:

[0037] The terminal receives dispatch instructions from the server and presents real-time information to administrators and drivers of the organization managing the transportation services. This information includes locations where demand is expected to be high and recommended dispatch times. Based on this information, administrators issue instructions to efficiently allocate taxis and other modes of transportation.

[0038] User roles:

[0039] As a user, the taxi driver follows instructions from the terminal, travels to the appropriate location and time, and waits for passengers. For example, if server analysis predicts a high demand for transportation around a particular station after an event, the driver will travel to that station in advance to prepare for passenger pickup. In this way, passenger waiting times are reduced, and efficient operation is achieved.

[0040] This system will improve the efficiency of transportation operations, reducing wasted time and fuel consumption. Operators will be able to improve customer satisfaction and operational efficiency by responding quickly and appropriately.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server collects necessary data from event information sites and weather forecast sites on the internet. Using web scraping and APIs, it stores the name, date, time, location, and weather information of local events in a database.

[0044] Step 2:

[0045] The server inputs the collected event and weather information into an AI model for analysis. The AI ​​model uses a time-series forecasting algorithm to identify times and locations where demand is likely to be high. For example, it takes into account the time a concert ends and changes in the weather throughout the day.

[0046] Step 3:

[0047] Based on the analysis results, the server generates dispatch instructions that correspond to the predicted demand. These instructions are set to place taxis in locations where high demand is predicted, within a specific time frame.

[0048] Step 4:

[0049] The terminal displays dispatch instructions received from the server on the taxi company's administrator dashboard in real time. The administrator uses this information to develop the optimal dispatch plan and send specific instructions to the drivers.

[0050] Step 5:

[0051] The taxi driver, acting as the user, heads to the designated time and location based on instructions from the terminal. The driver uses a map application to confirm their route while traveling, in order to efficiently acquire passengers.

[0052] Step 6:

[0053] The server collects dispatch data for the day and feeds it back into the AI ​​model. This result is used to retrain the model and improve the accuracy of future predictions.

[0054] (Example 1)

[0055] 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."

[0056] In the operation of transportation services, real-time demand forecasting and prompt dispatching instructions based on that forecast are essential for efficient dispatching. However, conventional systems have insufficient analysis of event and weather information, making it difficult to make accurate demand forecasts. As a result, this has led to wasted transportation services, increased fuel consumption, and a decline in customer service.

[0057] 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.

[0058] In this invention, the server includes means for collecting event-related information and weather-related information, means for analyzing the event-related information and weather-related information to predict people's movements, and means for performing the analysis based on prompt sentences using the generated AI model. This enables accurate prediction of people's movements and efficient generation of dispatch instructions based on those predictions.

[0059] "Event information" refers to data including the date, time, location, and scale of events and gatherings held in the local area.

[0060] "Weather information" refers to data including weather conditions, probability of precipitation, temperature, wind speed, etc., for a specific region on a daily basis.

[0061] A "generative AI model" is an algorithm or machine learning model designed to analyze specific patterns or trends and make predictions or decisions based on unverified data.

[0062] A "prompt" is a form of instruction or question entered into a generative AI model when performing analysis or generation.

[0063] A "server" is a computer system or data processing device that performs tasks such as data collection, analysis, and instruction generation.

[0064] "Transportation operation instructions" are instructions to optimize the deployment and operation of transportation at a specific location and time.

[0065] "Feedback" refers to the input of information used to improve models and methods based on actual results obtained in relation to the prediction results generated by the system.

[0066] "Real-time geographical location information" refers to information that can instantly update data indicating the current location, and is used for the efficient operation of transportation systems.

[0067] This system analyzes event and weather information to predict people's movements and optimize transportation scheduling. The system primarily consists of servers, terminals, and users.

[0068] Server role:

[0069] The server collects event and weather information via the internet. Specifically, it obtains event schedules from an event information API and weather data such as precipitation probability and temperature from a weather forecast API. Based on this data, the server uses a generative AI model to input prompt messages and make future predictions. The prompt message would be something like, "Based on the event information and weather conditions for this date and time, please predict the demand for transportation." From the generated predictions, the server estimates when and where taxi demand will increase and creates dispatch instructions.

[0070] Terminal role:

[0071] The terminal receives dispatch instructions from the server and provides them to transportation managers and drivers in real time. Along with the dispatch instructions, the terminal displays geographical location information and recommended dispatch times, enabling efficient allocation of transportation.

[0072] User roles:

[0073] A prime example of a user would be a taxi driver. Drivers follow instructions from their terminal and select the optimal route while checking real-time geographical location information to arrive at locations where demand is predicted on time. For example, dispatch instructions based on the scheduled end time of an event allow drivers to wait at the designated location before that time and efficiently pick up passengers.

[0074] By utilizing this specific example, it is possible to reduce people's waiting times and decrease wasted fuel. Through an efficient dispatch process across the entire system, operational efficiency and customer service will be improved.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The server sends requests to event information APIs and weather forecast APIs via the internet to retrieve information about events and weather conditions. Inputs include specific regions and dates, and outputs include the date, time, location, and scale of the event, as well as weather data (weather, probability of precipitation, temperature, etc.). This information is stored in a database on the server and used in subsequent analysis steps.

[0078] Step 2:

[0079] The server generates prompts to be input into the AI ​​model using the collected event and weather information. For example, it might create a prompt such as, "Based on the event information and weather conditions for this date and time, predict the demand for transportation." Based on this prompt, the AI ​​model predicts the flow of people and outputs predictions for the times and locations where demand will be highest.

[0080] Step 3:

[0081] The server analyzes the prediction results obtained from the generated AI model and generates transportation dispatch instructions. Inputs include predicted demand information and real-time geographical location information. The output is materialized as dispatch instructions for a specific location and time, including which driver should go where.

[0082] Step 4:

[0083] The terminal receives dispatch instructions from the server and notifies transportation service managers and drivers in real time. At the same time, the terminal's display screen shows further details such as recommended routes and estimated arrival times. This provides drivers with the information they need to take the most appropriate dispatch actions.

[0084] Step 5:

[0085] The user, a taxi driver, follows dispatch instructions on the terminal and travels to a location where demand is predicted at the specified time. Input data includes geographical information and estimated arrival time from the terminal, and during actual operation, the system efficiently moves using GPS location tracking and navigation functions. The output is improved passenger efficiency for clients and reduced waiting times.

[0086] (Application Example 1)

[0087] 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."

[0088] In recent years, with the advancement of autonomous driving technology, the efficient operation of transportation systems has become a critical issue. In particular, there is a need to accurately predict the locations and times where demand for transportation fluctuates and to optimize operations. However, conventional systems have not adequately considered event information and weather information for efficient dispatching, resulting in problems such as wasted travel and resource consumption. Therefore, the present invention aims to solve these problems and provide an efficient dispatching system using autonomous vehicles.

[0089] 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.

[0090] In this invention, the server includes means for collecting event information and weather information; means for analyzing the event information and weather information to predict the locations and times when people will gather and predict the demand for transportation; means for generating dispatch instructions for autonomous vehicles based on the prediction results; and means for notifying the control system of the autonomous vehicles of the dispatch instructions. This makes it possible to predict the demand for transportation with high accuracy and realize an efficient dispatch plan without waste.

[0091] "Event information" refers to information related to events and gatherings held in a local area, and it has a significant impact on people's movement and gatherings.

[0092] "Weather information" refers to information about the weather on a given day, such as temperature, precipitation, and wind speed, and is a factor that affects people's going out and traveling.

[0093] "Demand for transportation" is an indicator that shows the degree of demand for means of transport such as taxis, buses, and trains at a specific location and time.

[0094] An "autonomous vehicle" refers to a vehicle that can be operated and driven by itself without human intervention using AI technology, and is expected to be an efficient means of transportation.

[0095] "Dispatch instructions" refer to command information that instructs operators or equipment of a means of transport to move to a specific location, and are used to improve operational efficiency.

[0096] A "control system" refers to all hardware and software used to manage and regulate the operation of transportation or related equipment.

[0097] This invention relates to a system that predicts the locations and times when people gather based on event information and weather information, and enables the efficient dispatch of autonomous vehicles.

[0098] The server first collects event and weather information using public databases and information services on the internet. This information is integrated as "event information" and "weather information," and analyzed using a generative AI model. Based on the analysis results, it predicts areas and times when pedestrian traffic will increase and estimates the demand for transportation. To perform this process, the server uses a high-performance data processing unit and software specialized for AI processing. Specific software includes libraries suitable for data analysis (for example, Python's Pandas and Scikit-learn).

[0099] The terminal receives dispatch instructions transmitted from the server. In this invention, dispatch instructions are directly notified to the control system of the autonomous vehicle, eliminating the need for human intervention as in conventional systems. The terminal can transmit real-time geographical location information and travel route to the traffic system, enabling the autonomous vehicle to efficiently move to locations where people gather and prepare to pick up passengers.

[0100] Users, particularly operators and providers of autonomous vehicles, can use these automated systems to improve vehicle operational efficiency and make more effective use of resources. For example, if a server predicts increased demand around a specific concert venue on a weekend night based on data, autonomous vehicles can wait in that area in advance to smoothly alleviate congestion after the event ends.

[0101] Examples of prompt statements include the following:

[0102] "Based on the following data, predict peak demand and generate the optimal ride-hailing plan: Event information: {event_info}, Weather information: {weather_info}"

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The server collects event and weather information from the internet. Specifically, it sends requests to public databases and information websites and retrieves the data obtained as responses. It receives responses from various APIs as input and outputs them as JSON data.

[0106] Step 2:

[0107] The server analyzes the collected event and weather information using an AI model. This analysis preprocesses the information and uses statistical methods and machine learning algorithms to predict the times and places where people are likely to gather. The input is event and weather information in JSON format, which is converted into feature data and input into the AI ​​model, generating a prediction of human flow as output.

[0108] Step 3:

[0109] The server generates dispatch instructions for autonomous vehicles based on the prediction results. In this step, it identifies locations and times when demand for transportation will be high based on the prediction results, and creates the optimal travel route and arrival time. It receives the prediction results of the AI ​​model as input and generates dispatch instruction data as output.

[0110] Step 4:

[0111] The server notifies the terminal of the generated dispatch instructions. The terminal forwards the dispatch instructions received from the server to the control system of the autonomous vehicle, which adjusts the vehicle's location and planned route in real time. The input is dispatch instruction data, and the output is instructions to the control system.

[0112] Step 5:

[0113] Users monitor the operational status of autonomous vehicles via a terminal. Operators and service providers refer to the geographical location information and vehicle status provided on the terminal and adjust operational plans as needed. Input is real-time information from the terminal, and output is to support the operational manager's decision-making.

[0114] 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.

[0115] This invention combines a system for efficiently dispatching transportation services with an emotion engine that recognizes user emotions. This makes it possible to provide personalized services tailored to the user's emotional state.

[0116] Server role:

[0117] The server collects event and weather information in real time and analyzes it to predict where and when people will gather. Furthermore, an emotion engine collects user emotion data and incorporates the analysis results into the forecasting of transportation demand. For example, if a user is excited during an event, this emotion data is taken into account to predict the times when services are most likely to be needed.

[0118] Terminal role:

[0119] Dispatch instructions sent from the server and emotion-based, tailored service information are displayed on a dashboard for transportation operators. Operators can take actions to improve the experience by focusing on specific user groups, such as providing faster taxi services to users with high stress levels.

[0120] User roles:

[0121] Through an interface provided by the emotion engine, users' emotional states are reflected in the service. Drivers receive suggestions to choose specific driving methods based on the user's emotional state. For example, a user feeling anxious can be offered relaxing music or lighting in the car.

[0122] This system allows transportation operators to provide more precise service by utilizing unique emotional data in addition to conventional dispatch efficiency. This will enable them to improve the passenger experience and increase customer satisfaction.

[0123] The following describes the processing flow.

[0124] Step 1:

[0125] The server collects event and weather information from the internet. It uses web scraping or APIs to retrieve local event schedules and weather forecasts, and stores them in a database.

[0126] Step 2:

[0127] The server collects data to recognize the user's emotions. Using an emotion engine, it retrieves emotional data from the user's mobile app or wearable device. This includes heart rate, voice tone, and facial expressions.

[0128] Step 3:

[0129] The server analyzes collected event information, weather information, and sentiment data. This analysis identifies times and locations where people are likely to gather, and adjusts service content to reflect the emotional state of users. For example, if it determines that many users are feeling stressed towards the end of an event, it recommends prompt vehicle dispatch.

[0130] Step 4:

[0131] The server notifies transportation operators of generated dispatch instructions and emotion-based service instructions. This information includes dispatch planning for specific locations and methods to improve the user experience.

[0132] Step 5:

[0133] The terminal displays instructions from the server on an operator's dashboard. Based on this information, operators can adjust dispatch plans and service delivery in real time. For example, they can adjust the temperature and music inside the vehicle before passengers board.

[0134] Step 6:

[0135] Users are configured to receive services based on their emotional state. Taxi drivers review the user's recommended services and customize the experience provided during the ride. For example, the driver might play relaxing music or choose a route that suits the user's preferences.

[0136] Step 7:

[0137] The server collects dispatch results and user feedback for the day. Based on this data, the AI ​​model and emotion engine are continuously improved to enhance the accuracy of future services.

[0138] (Example 2)

[0139] 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".

[0140] The efficiency and accuracy of transportation dispatching currently depend on existing algorithms and data analysis methods, making it difficult to adequately respond to real-time, changing demand. Furthermore, conventional systems cannot provide services that take into account users' emotional states, making it difficult to increase user satisfaction. Therefore, it is necessary to solve these problems by more accurately predicting transportation demand and providing services that respond to users' emotional states.

[0141] 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.

[0142] In this invention, the server includes means for collecting event information and weather information, means for analyzing the event information and weather information to predict the locations and times when people gather, and means for collecting and quantifying the emotional state of users as data. This enables efficient dispatching of transportation and the provision of services tailored to the individual emotions of users.

[0143] "Event information" refers to data about events and gatherings held at specific locations and times, and is used to predict crowd sizes in the ride-hailing system.

[0144] "Weather information" refers to data on current or predicted weather conditions, such as temperature, precipitation, and wind speed, which are considered in order to forecast the demand for transportation.

[0145] "User emotional state" refers to information that indicates the psychological or emotional state an individual user experiences at a specific time, and can be used to personalize service delivery.

[0146] "Dispatch instructions" are instructions regarding the arrangement of transportation methods, generated based on predicted and analyzed data, and are notified to the operator.

[0147] "Notifying operators" means delivering generated instructions and information to transportation managers and drivers, which is necessary for providing appropriate ride-hailing services.

[0148] "Continuously improving analytical methods" means regularly reviewing the system's algorithms and processes based on past predictions and results, and continuously improving its performance.

[0149] "Real-time geographical location information" refers to information that indicates the current location of transportation methods and users, enabling a rapid response in ride-hailing and service provision.

[0150] "Service suggestion" refers to the act of providing special services or suggestions tailored to the user's current emotional state, and is used to improve the user experience.

[0151] This invention provides a more personalized service by combining a transportation dispatch system with technology that recognizes and utilizes user emotions. The server collects event and weather information in real time via a communication network. Data collection utilizes data sources accessible via the internet, and the collected data is stored in databases such as MySQL® or PostgreSQL. This information is analyzed using Apache® Hadoop and the Python Pandas library to predict where and when people will gather.

[0152] The server also uses an emotion engine to collect user emotional state data. This emotion engine receives user input via a smartphone app or web app, quantifies that input data, and stores it. For example, if a user inputs "relaxed," their emotional state is stored as the number "70."

[0153] The terminal displays dispatch instructions and service guidance based on sentiment data sent from the server on a dashboard for transportation operators. This dashboard is used by operators to take actions focused on specific user groups. Technically, JavaScript® libraries such as React and Vue.js are used to create the dashboard.

[0154] Users input their emotional state using an interface provided through an emotion engine, and customized services are then offered. Drivers receive suggestions based on the user's emotions and adjust the in-car environment accordingly. For example, by prompting the generative AI model with "What kind of in-car environment do you want when you want to relax?", they can receive advice on soft music and lighting settings.

[0155] This allows transportation operators to provide accurate dispatching along with services tailored to user needs, thereby improving passenger experience and customer satisfaction.

[0156] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0157] Step 1:

[0158] The server retrieves event and weather information via the internet. It uses APIs and scraping techniques to store the retrieved information in a database. Input is data from a Web API, and output is raw event and weather data stored in the database.

[0159] Step 2:

[0160] The server analyzes collected event and weather information. It uses the Python Pandas library to clean and format the data, then applies an analysis algorithm to predict where and when people will gather. The input to this analysis is the formatted data, and the output is the future locations and times of human congestion derived from the prediction model.

[0161] Step 3:

[0162] The server uses an emotion engine to collect and quantify emotional data from users. The emotional state transmitted by the user through the app or web interface serves as input, which is then output and stored as numerical data. This process objectifies the user's subjective emotional input.

[0163] Step 4:

[0164] The server integrates prediction results and sentiment data to generate dispatch instructions for transportation. Using a generation AI model, it calculates the appropriate number of vehicles and timing, and notifies operators of these instructions. The input is integrated data, and the output is a specific dispatch plan and suggested time slots.

[0165] Step 5:

[0166] The terminal displays dispatch instructions received from the server and sentiment-based service information on a dashboard. This information is visualized using frontend technologies such as React and Vue.js. The input is the received dataset, and the output is an administration screen for operators.

[0167] Step 6:

[0168] The user receives services tailored to their emotional state through a provided interface. At this stage, the driver provides specific services based on instructions received from the server. As an example of a prompt to the generative AI model, the user might input, "Please tell me what kind of in-car environment would make you feel relaxed," and the system would provide an appropriate environment based on the response. The input is the user's emotional data, and the output is a real-time, customized service based on that data.

[0169] (Application Example 2)

[0170] 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 device 14 will be referred to as the "terminal."

[0171] Traditional ride-hailing systems for transportation services relied solely on geographical information and time-of-day demand forecasts, without considering the user's emotional state. This often overlooked factors influencing user satisfaction and the quality of their experience, hindering improvements in satisfaction. Furthermore, providing personalized services to individual users proved difficult.

[0172] 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.

[0173] In this invention, the server includes means for collecting event information and weather information, means for analyzing the event information and weather information to predict the locations and times when people gather, and means for recognizing the emotional state of the user and using the emotional data to predict the demand for transportation. This makes it possible to provide a personalized ride-hailing service that responds to the user's emotions.

[0174] "Event information" refers to data about events and activities held at a specific date, time, and location.

[0175] "Weather information" refers to data related to weather conditions, such as temperature, precipitation, and wind speed.

[0176] "User emotional state" refers to data that reflects an individual's psychological and emotional state.

[0177] "Demand forecasting" is the process of performing analysis and calculations to predict future demand and needs.

[0178] "Vehicle dispatch instructions" are instructions given to operators to determine the location and travel time of the means of transport.

[0179] "Transportation" refers to vehicles and transportation systems used for transporting individuals or goods.

[0180] "Service information" refers to information about actions and features provided to improve the user experience.

[0181] A "server" is a networked computer system used for processing, analyzing, and storing data.

[0182] An "emotion engine" is a technology or algorithm used to detect and analyze a user's emotional state.

[0183] The server collects event and weather information in real time, analyzes it, and predicts the places and times when people are likely to gather. Furthermore, it uses an emotion engine to collect users' emotional states in real time and incorporates this into demand forecasting. The server fuses this data to generate optimal dispatch instructions. By using cloud platforms such as Google Cloud and AWS, it is possible to process and analyze large amounts of data. In addition, the emotion engine can use an algorithm developed in-house.

[0184] The terminal displays dispatch instructions sent from the server and service information tailored based on the user's emotions. Through the dashboard, the operator can take actions to provide services that match the user's emotional state (e.g., relaxing music or lighting). Typical terminals used by operators include general-purpose computers and tablets.

[0185] Users communicate their emotional state to the emotion engine via their smartphones, resulting in optimized ride-hailing services. The smartphones are equipped with the Emotion SDK, which detects emotions in real time. Furthermore, the autonomous vehicle interface provides services that reflect this emotional information.

[0186] As a concrete example, when a user calls a taxi after attending an evening event, the server analyzes the number of event attendees and related weather information to predict when taxi demand will be highest. Simultaneously, it retrieves emotional data from the user's smartphone, and if it determines that the user needs to relax, it adjusts the music and environment in the car. This allows the user to travel to their destination comfortably.

[0187] Using a generative AI model, prompts such as "This user is feeling anxious. How should you adjust the in-car environment?" or "Based on the user's emotional data, please select the most relaxing route" are generated based on the user's emotional data.

[0188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0189] Step 1:

[0190] The server collects event and weather information. Event schedules and weather data are provided as input, and this information is stored in a database in real time. The output is digital data of the collected event and weather information. In this step, the information is automatically retrieved via a web API.

[0191] Step 2:

[0192] The server analyzes collected event and weather information to predict where and when people will gather. It uses the information from the previous step as input and applies an analysis algorithm. The output generates predicted crowd levels for specific time periods and geographical locations. Machine learning algorithms are used for processing this data.

[0193] Step 3:

[0194] The server recognizes the user's emotional state and collects data on it. Input consists of physiological and audio data transmitted from the user's smartphone. Output is analytical data related to the user's emotional state. This data is processed by the emotion engine using the Emotion SDK.

[0195] Step 4:

[0196] The server predicts demand for transportation using customer acquisition forecast data and sentiment data. This includes forecast data and sentiment data obtained from steps 2 and 3 as input data, and optimized demand forecast data is generated as output. In this step, the generated AI model is utilized to integrate demand forecasting and sentiment analysis.

[0197] Step 5:

[0198] The server generates dispatch instructions and notifies operators. Demand forecast data is used as input. Output includes specific dispatch instructions sent to drivers and service recommendations based on sentiment data. At this stage, information is sent to the operator's dashboard via a notification system.

[0199] Step 6:

[0200] Users receive individually optimized ride-hailing services via their smartphones. Input consists of service adjustment information based on prompts. Output is a personalized ride-hailing experience. This process allows users to travel more comfortably and efficiently.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Second Embodiment]

[0205] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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".

[0217] This invention provides a system that predicts the flow of people necessary for the effective operation of transportation and dispatches appropriate vehicles based on that prediction. The system consists of a server, terminals, and users.

[0218] Server role:

[0219] The server collects event and weather information from the internet. For example, it obtains concert schedules for the area and the probability of precipitation for the day from weather forecast websites. Using this information, an AI model analyzes it to predict the times and places where people are likely to gather. Based on the analysis results, it predicts that demand will increase at specific locations and times and generates dispatch instructions accordingly.

[0220] Terminal role:

[0221] The terminal receives dispatch instructions from the server and presents real-time information to administrators and drivers of the organization managing the transportation services. This information includes locations where demand is expected to be high and recommended dispatch times. Based on this information, administrators issue instructions to efficiently allocate taxis and other modes of transportation.

[0222] User roles:

[0223] As a user, the taxi driver follows instructions from the terminal, travels to the appropriate location and time, and waits for passengers. For example, if server analysis predicts a high demand for transportation around a particular station after an event, the driver will travel to that station in advance to prepare for passenger pickup. In this way, passenger waiting times are reduced, and efficient operation is achieved.

[0224] This system will improve the efficiency of transportation operations, reducing wasted time and fuel consumption. Operators will be able to improve customer satisfaction and operational efficiency by responding quickly and appropriately.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] The server collects necessary data from event information sites and weather forecast sites on the internet. Using web scraping and APIs, it stores the name, date, time, location, and weather information of local events in a database.

[0228] Step 2:

[0229] The server inputs the collected event and weather information into an AI model for analysis. The AI ​​model uses a time-series forecasting algorithm to identify times and locations where demand is likely to be high. For example, it takes into account the time a concert ends and changes in the weather throughout the day.

[0230] Step 3:

[0231] Based on the analysis results, the server generates dispatch instructions that correspond to the predicted demand. These instructions are set to place taxis in locations where high demand is predicted, within a specific time frame.

[0232] Step 4:

[0233] The terminal displays dispatch instructions received from the server on the taxi company's administrator dashboard in real time. The administrator uses this information to develop the optimal dispatch plan and send specific instructions to the drivers.

[0234] Step 5:

[0235] The taxi driver, acting as the user, heads to the designated time and location based on instructions from the terminal. The driver uses a map application to confirm their route while traveling, in order to efficiently acquire passengers.

[0236] Step 6:

[0237] The server collects dispatch data for the day and feeds it back into the AI ​​model. This result is used to retrain the model and improve the accuracy of future predictions.

[0238] (Example 1)

[0239] 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".

[0240] In the operation of transportation services, real-time demand forecasting and prompt dispatching instructions based on that forecast are essential for efficient dispatching. However, conventional systems have insufficient analysis of event and weather information, making it difficult to make accurate demand forecasts. As a result, this has led to wasted transportation services, increased fuel consumption, and a decline in customer service.

[0241] 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.

[0242] In this invention, the server includes means for collecting event-related information and weather-related information, means for analyzing the event-related information and weather-related information to predict people's movements, and means for performing the analysis based on prompt sentences using the generated AI model. This enables accurate prediction of people's movements and efficient generation of dispatch instructions based on those predictions.

[0243] "Event information" refers to data including the date, time, location, and scale of events and gatherings held in the local area.

[0244] "Weather information" refers to data including weather conditions, probability of precipitation, temperature, wind speed, etc., for a specific region on a daily basis.

[0245] A "generative AI model" is an algorithm or machine learning model designed to analyze specific patterns or trends and make predictions or decisions based on unverified data.

[0246] A "prompt" is a form of instruction or question entered into a generative AI model when performing analysis or generation.

[0247] A "server" is a computer system or data processing device that performs tasks such as data collection, analysis, and instruction generation.

[0248] "Transportation operation instructions" are instructions to optimize the deployment and operation of transportation at a specific location and time.

[0249] "Feedback" refers to the input of information used to improve models and methods based on actual results obtained in relation to the prediction results generated by the system.

[0250] "Real-time geographical location information" refers to information that can instantly update data indicating the current location, and is used for the efficient operation of transportation systems.

[0251] This system analyzes event and weather information to predict people's movements and optimize transportation scheduling. The system primarily consists of servers, terminals, and users.

[0252] Server role:

[0253] The server collects event and weather information via the internet. Specifically, it obtains event schedules from an event information API and weather data such as precipitation probability and temperature from a weather forecast API. Based on this data, the server uses a generative AI model to input prompt messages and make future predictions. The prompt message would be something like, "Based on the event information and weather conditions for this date and time, please predict the demand for transportation." From the generated predictions, the server estimates when and where taxi demand will increase and creates dispatch instructions.

[0254] Terminal role:

[0255] The terminal receives dispatch instructions from the server and provides them to transportation managers and drivers in real time. Along with the dispatch instructions, the terminal displays geographical location information and recommended dispatch times, enabling efficient allocation of transportation.

[0256] User roles:

[0257] A prime example of a user would be a taxi driver. Drivers follow instructions from their terminal and select the optimal route while checking real-time geographical location information to arrive at locations where demand is predicted on time. For example, dispatch instructions based on the scheduled end time of an event allow drivers to wait at the designated location before that time and efficiently pick up passengers.

[0258] By utilizing this specific example, it is possible to reduce people's waiting times and decrease wasted fuel. Through an efficient dispatch process across the entire system, operational efficiency and customer service will be improved.

[0259] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0260] Step 1:

[0261] The server sends requests to event information APIs and weather forecast APIs via the internet to retrieve information about events and weather conditions. Inputs include specific regions and dates, and outputs include the date, time, location, and scale of the event, as well as weather data (weather, probability of precipitation, temperature, etc.). This information is stored in a database on the server and used in subsequent analysis steps.

[0262] Step 2:

[0263] The server generates prompts to be input into the AI ​​model using the collected event and weather information. For example, it might create a prompt such as, "Based on the event information and weather conditions for this date and time, predict the demand for transportation." Based on this prompt, the AI ​​model predicts the flow of people and outputs predictions for the times and locations where demand will be highest.

[0264] Step 3:

[0265] The server analyzes the prediction results obtained from the generated AI model and generates transportation dispatch instructions. Inputs include predicted demand information and real-time geographical location information. The output is materialized as dispatch instructions for a specific location and time, including which driver should go where.

[0266] Step 4:

[0267] The terminal receives dispatch instructions from the server and notifies transportation service managers and drivers in real time. At the same time, the terminal's display screen shows further details such as recommended routes and estimated arrival times. This provides drivers with the information they need to take the most appropriate dispatch actions.

[0268] Step 5:

[0269] The user, a taxi driver, follows dispatch instructions on the terminal and travels to a location where demand is predicted at the specified time. Input data includes geographical information and estimated arrival time from the terminal, and during actual operation, the system efficiently moves using GPS location tracking and navigation functions. The output is improved passenger efficiency for clients and reduced waiting times.

[0270] (Application Example 1)

[0271] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0272] In recent years, with the advancement of autonomous driving technology, the efficient operation of transportation systems has become a critical issue. In particular, there is a need to accurately predict the locations and times where demand for transportation fluctuates and to optimize operations. However, conventional systems have not adequately considered event information and weather information for efficient dispatching, resulting in problems such as wasted travel and resource consumption. Therefore, the present invention aims to solve these problems and provide an efficient dispatching system using autonomous vehicles.

[0273] 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.

[0274] In this invention, the server includes means for collecting event information and weather information; means for analyzing the event information and weather information to predict the locations and times when people will gather and predict the demand for transportation; means for generating dispatch instructions for autonomous vehicles based on the prediction results; and means for notifying the control system of the autonomous vehicles of the dispatch instructions. This makes it possible to predict the demand for transportation with high accuracy and realize an efficient dispatch plan without waste.

[0275] "Event information" refers to information related to events and gatherings held in a local area, and it has a significant impact on people's movement and gatherings.

[0276] "Weather information" refers to information about the weather on a given day, such as temperature, precipitation, and wind speed, and is a factor that affects people's going out and traveling.

[0277] "Demand for transportation" is an indicator that shows the degree of demand for means of transport such as taxis, buses, and trains at a specific location and time.

[0278] An "autonomous vehicle" refers to a vehicle that can be operated and driven by itself without human intervention using AI technology, and is expected to be an efficient means of transportation.

[0279] "Vehicle allocation instruction" refers to the command information transmitted to the operator or device of a means of transportation to move to a specific location, and is used to improve operation efficiency.

[0280] "Control system" refers to all hardware and software for managing and adjusting the operations of a means of transportation or related devices.

[0281] The present invention relates to a system that predicts the locations and times where people gather based on event information and weather information, and performs efficient vehicle allocation for autonomous vehicles.

[0282] First, the server collects event information and weather information by using public databases and information providing services on the Internet. This information is integrated as "event information" and "weather information" and analyzed using a generated AI model. From the results of the analysis, the areas and time zones where the flow of people increases are predicted, and the demand for means of transportation is estimated. To execute this process, the server uses a high-performance data processing unit and software specialized for AI processing. Specific software includes libraries suitable for data analysis (for example, Pandas and Scikit-learn in Python).

[0283] The terminal receives the vehicle allocation instruction transmitted from the server. In the present invention, since the vehicle allocation instruction is directly notified to the control system of the autonomous vehicle, human intervention is not required as in the prior art. The terminal can transmit real-time geographical location information and movement routes to the traffic system, enabling the autonomous vehicle to efficiently move to the locations where people gather and be ready to pick up passengers.

[0284] Users, especially the operators and drivers of autonomous vehicles, can use these automated mechanisms to improve the operating efficiency of the vehicles and enable effective utilization of resources. For example, on weekend nights, if the server predicts an increase in demand around a specific concert venue from the data, the autonomous vehicle can wait in that area in advance and smoothly resolve the congestion after the event.

[0285] Examples of prompt texts include the following:

[0286] "Please predict the peak demand based on the following data and generate an optimal vehicle distribution plan. Event information: {event_info}, weather information: {weather_info}"

[0287] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0288] Step 1:

[0289] The server collects event information and weather information from the Internet. Specifically, it sends requests to public databases and information-providing sites and acquires the data obtained as responses. It receives responses from various APIs as input and outputs this as data in JSON format.

[0290] Step 2:

[0291] The server analyzes the collected event information and weather information using an AI model. In this analysis, the information is preprocessed, and statistical methods and machine learning algorithms are used to predict the times and places where people are likely to gather. The input is event information and weather information in JSON format, which is converted into feature data and input into the AI model, and a prediction result of people's flow is generated as the output.

[0292] Step 3:

[0293] The server generates a vehicle distribution instruction for autonomous vehicles based on the prediction result. In this step, based on the prediction result, the points and times where the demand for transportation means increases are specified, and an optimal movement route and arrival timing are created. The server receives the prediction result of the AI model as input and generates vehicle distribution instruction data as the output.

[0294] Step 4:

[0295] The server notifies the terminal of the generated dispatch instructions. The terminal forwards the dispatch instructions received from the server to the control system of the autonomous vehicle, which adjusts the vehicle's location and planned route in real time. The input is dispatch instruction data, and the output is instructions to the control system.

[0296] Step 5:

[0297] Users monitor the operational status of autonomous vehicles via a terminal. Operators and service providers refer to the geographical location information and vehicle status provided on the terminal and adjust operational plans as needed. Input is real-time information from the terminal, and output is to support the operational manager's decision-making.

[0298] 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.

[0299] This invention combines a system for efficiently dispatching transportation services with an emotion engine that recognizes user emotions. This makes it possible to provide personalized services tailored to the user's emotional state.

[0300] Server role:

[0301] The server collects event and weather information in real time and analyzes it to predict where and when people will gather. Furthermore, an emotion engine collects user emotion data and incorporates the analysis results into the forecasting of transportation demand. For example, if a user is excited during an event, this emotion data is taken into account to predict the times when services are most likely to be needed.

[0302] Terminal role:

[0303] Display the vehicle dispatching instructions sent from the server and the service information adjusted based on emotions on the dashboard for the operators of transportation means. The operator can take actions to improve the experience, focusing on a specific user group. For example, provide a rapid taxi service for users with a high stress level.

[0304] Role of the user:

[0305] The user's emotional state is reflected in the service through the interface provided by the emotion engine. The driver receives suggestions to select a specific driving method based on the user's emotional state. For example, for a user feeling anxious, music or lighting that can help relax in the vehicle can be provided.

[0306] With this system, the operators of transportation means can utilize their own emotional data in addition to the conventional vehicle dispatching efficiency, enabling more precise service provision. This can aim to improve the passenger experience and customer satisfaction.

[0307] The following describes the processing flow.

[0308] Step 1:

[0309] The server collects event information and weather information from the Internet. Using web scraping or APIs, obtain the event schedule and weather forecast related to the region, and save them in the database.

[0310] Step 2:

[0311] The server collects data for recognizing the user's emotions. Using the emotion engine, obtain emotion data from the user's mobile app or wearable device. This includes heart rate, voice tone, facial expressions, etc.

[0312] Step 3:

[0313] The server analyzes collected event information, weather information, and sentiment data. This analysis identifies times and locations where people are likely to gather, and adjusts service content to reflect the emotional state of users. For example, if it determines that many users are feeling stressed towards the end of an event, it recommends prompt vehicle dispatch.

[0314] Step 4:

[0315] The server notifies transportation operators of generated dispatch instructions and emotion-based service instructions. This information includes dispatch planning for specific locations and methods to improve the user experience.

[0316] Step 5:

[0317] The terminal displays instructions from the server on an operator's dashboard. Based on this information, operators can adjust dispatch plans and service delivery in real time. For example, they can adjust the temperature and music inside the vehicle before passengers board.

[0318] Step 6:

[0319] Users are configured to receive services based on their emotional state. Taxi drivers review the user's recommended services and customize the experience provided during the ride. For example, the driver might play relaxing music or choose a route that suits the user's preferences.

[0320] Step 7:

[0321] The server collects dispatch results and user feedback for the day. Based on this data, the AI ​​model and emotion engine are continuously improved to enhance the accuracy of future services.

[0322] (Example 2)

[0323] 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".

[0324] The efficiency and accuracy of transportation dispatching currently depend on existing algorithms and data analysis methods, making it difficult to adequately respond to real-time, changing demand. Furthermore, conventional systems cannot provide services that take into account users' emotional states, making it difficult to increase user satisfaction. Therefore, it is necessary to solve these problems by more accurately predicting transportation demand and providing services that respond to users' emotional states.

[0325] 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.

[0326] In this invention, the server includes means for collecting event information and weather information, means for analyzing the event information and weather information to predict the locations and times when people gather, and means for collecting and quantifying the emotional state of users as data. This enables efficient dispatching of transportation and the provision of services tailored to the individual emotions of users.

[0327] "Event information" refers to data about events and gatherings held at specific locations and times, and is used to predict crowd sizes in the ride-hailing system.

[0328] "Weather information" refers to data on current or predicted weather conditions, such as temperature, precipitation, and wind speed, which are considered in order to forecast the demand for transportation.

[0329] "User emotional state" refers to information that indicates the psychological or emotional state an individual user experiences at a specific time, and can be used to personalize service delivery.

[0330] "Dispatch instructions" are instructions regarding the arrangement of transportation methods, generated based on predicted and analyzed data, and are notified to the operator.

[0331] "Notifying operators" means delivering generated instructions and information to transportation managers and drivers, which is necessary for providing appropriate ride-hailing services.

[0332] "Continuously improving analytical methods" means regularly reviewing the system's algorithms and processes based on past predictions and results, and continuously improving its performance.

[0333] "Real-time geographical location information" refers to information that indicates the current location of transportation methods and users, enabling a rapid response in ride-hailing and service provision.

[0334] "Service suggestion" refers to the act of providing special services or suggestions tailored to the user's current emotional state, and is used to improve the user experience.

[0335] This invention provides a more personalized service by combining a transportation dispatch system with technology that recognizes and utilizes user emotions. The server collects event and weather information in real time via a communication network. Data collection utilizes data sources accessible via the internet, and the collected data is stored in databases such as MySQL or PostgreSQL. This information is analyzed using Apache Hadoop and the Python Pandas library to predict where and when people will gather.

[0336] The server also uses an emotion engine to collect user emotional state data. This emotion engine receives user input via a smartphone app or web app, quantifies that input data, and stores it. For example, if a user inputs "relaxed," their emotional state is stored as the number "70."

[0337] The terminal displays dispatch instructions and service guidance based on sentiment data sent from the server on a dashboard for transportation operators. This dashboard is used by operators to take actions focused on specific user groups. Technically, JavaScript libraries such as React and Vue.js are used to create the dashboard.

[0338] Users input their emotional state using an interface provided through an emotion engine, and customized services are then offered. Drivers receive suggestions based on the user's emotions and adjust the in-car environment accordingly. For example, by prompting the generative AI model with "What kind of in-car environment do you want when you want to relax?", they can receive advice on soft music and lighting settings.

[0339] This allows transportation operators to provide accurate dispatching along with services tailored to user needs, thereby improving passenger experience and customer satisfaction.

[0340] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0341] Step 1:

[0342] The server retrieves event and weather information via the internet. It uses APIs and scraping techniques to store the retrieved information in a database. Input is data from a Web API, and output is raw event and weather data stored in the database.

[0343] Step 2:

[0344] The server analyzes collected event and weather information. It uses the Python Pandas library to clean and format the data, then applies an analysis algorithm to predict where and when people will gather. The input to this analysis is the formatted data, and the output is the future locations and times of human congestion derived from the prediction model.

[0345] Step 3:

[0346] The server uses an emotion engine to collect and quantify emotional data from users. The emotional state transmitted by the user through the app or web interface serves as input, which is then output and stored as numerical data. This process objectifies the user's subjective emotional input.

[0347] Step 4:

[0348] The server integrates prediction results and sentiment data to generate dispatch instructions for transportation. Using a generation AI model, it calculates the appropriate number of vehicles and timing, and notifies operators of these instructions. The input is integrated data, and the output is a specific dispatch plan and suggested time slots.

[0349] Step 5:

[0350] The terminal displays dispatch instructions received from the server and sentiment-based service information on a dashboard. This information is visualized using frontend technologies such as React and Vue.js. The input is the received dataset, and the output is an administration screen for operators.

[0351] Step 6:

[0352] The user receives services tailored to their emotional state through a provided interface. At this stage, the driver provides specific services based on instructions received from the server. As an example of a prompt to the generative AI model, the user might input, "Please tell me what kind of in-car environment would make you feel relaxed," and the system would provide an appropriate environment based on the response. The input is the user's emotional data, and the output is a real-time, customized service based on that data.

[0353] (Application Example 2)

[0354] 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."

[0355] Traditional ride-hailing systems for transportation services relied solely on geographical information and time-of-day demand forecasts, without considering the user's emotional state. This often overlooked factors influencing user satisfaction and the quality of their experience, hindering improvements in satisfaction. Furthermore, providing personalized services to individual users proved difficult.

[0356] 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.

[0357] In this invention, the server includes means for collecting event information and weather information, means for analyzing the event information and weather information to predict the locations and times when people gather, and means for recognizing the emotional state of the user and using the emotional data to predict the demand for transportation. This makes it possible to provide a personalized ride-hailing service that responds to the user's emotions.

[0358] "Event information" refers to data about events and activities held at a specific date, time, and location.

[0359] "Weather information" refers to data related to weather conditions, such as temperature, precipitation, and wind speed.

[0360] "User emotional state" refers to data that reflects an individual's psychological and emotional state.

[0361] "Demand forecasting" is the process of performing analysis and calculations to predict future demand and needs.

[0362] "Vehicle dispatch instructions" are instructions given to operators to determine the location and travel time of the means of transport.

[0363] "Transportation" refers to vehicles and transportation systems used for transporting individuals or goods.

[0364] "Service information" refers to information about actions and features provided to improve the user experience.

[0365] A "server" is a networked computer system used for processing, analyzing, and storing data.

[0366] An "emotion engine" is a technology or algorithm used to detect and analyze a user's emotional state.

[0367] The server collects event and weather information in real time, analyzes it, and predicts the places and times when people are likely to gather. Furthermore, it uses an emotion engine to collect users' emotional states in real time and incorporates this into demand forecasting. The server fuses this data to generate optimal dispatch instructions. By using cloud platforms such as Google Cloud and AWS, it is possible to process and analyze large amounts of data. In addition, the emotion engine can use an algorithm developed in-house.

[0368] The terminal displays dispatch instructions sent from the server and service information tailored based on the user's emotions. Through the dashboard, the operator can take actions to provide services that match the user's emotional state (e.g., relaxing music or lighting). Typical terminals used by operators include general-purpose computers and tablets.

[0369] Users communicate their emotional state to the emotion engine via their smartphones, resulting in optimized ride-hailing services. The smartphones are equipped with the Emotion SDK, which detects emotions in real time. Furthermore, the autonomous vehicle interface provides services that reflect this emotional information.

[0370] As a concrete example, when a user calls a taxi after attending an evening event, the server analyzes the number of event attendees and related weather information to predict when taxi demand will be highest. Simultaneously, it retrieves emotional data from the user's smartphone, and if it determines that the user needs to relax, it adjusts the music and environment in the car. This allows the user to travel to their destination comfortably.

[0371] Using a generative AI model, prompts such as "This user is feeling anxious. How should you adjust the in-car environment?" or "Based on the user's emotional data, please select the most relaxing route" are generated based on the user's emotional data.

[0372] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0373] Step 1:

[0374] The server collects event and weather information. Event schedules and weather data are provided as input, and this information is stored in a database in real time. The output is digital data of the collected event and weather information. In this step, the information is automatically retrieved via a web API.

[0375] Step 2:

[0376] The server analyzes collected event and weather information to predict where and when people will gather. It uses the information from the previous step as input and applies an analysis algorithm. The output generates predicted crowd levels for specific time periods and geographical locations. Machine learning algorithms are used for processing this data.

[0377] Step 3:

[0378] The server recognizes the user's emotional state and collects data on it. Input consists of physiological and audio data transmitted from the user's smartphone. Output is analytical data related to the user's emotional state. This data is processed by the emotion engine using the Emotion SDK.

[0379] Step 4:

[0380] The server predicts demand for transportation using customer acquisition forecast data and sentiment data. This includes forecast data and sentiment data obtained from steps 2 and 3 as input data, and optimized demand forecast data is generated as output. In this step, the generated AI model is utilized to integrate demand forecasting and sentiment analysis.

[0381] Step 5:

[0382] The server generates dispatch instructions and notifies operators. Demand forecast data is used as input. Output includes specific dispatch instructions sent to drivers and service recommendations based on sentiment data. At this stage, information is sent to the operator's dashboard via a notification system.

[0383] Step 6:

[0384] Users receive individually optimized ride-hailing services via their smartphones. Input consists of service adjustment information based on prompts. Output is a personalized ride-hailing experience. This process allows users to travel more comfortably and efficiently.

[0385] 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.

[0386] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0387] 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.

[0388] [Third Embodiment]

[0389] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0390] 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.

[0391] 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).

[0392] 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.

[0393] 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.

[0394] 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).

[0395] 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.

[0396] 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.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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".

[0401] This invention provides a system that predicts the flow of people necessary for the effective operation of transportation and dispatches appropriate vehicles based on that prediction. The system consists of a server, terminals, and users.

[0402] Server role:

[0403] The server collects event and weather information from the internet. For example, it obtains concert schedules for the area and the probability of precipitation for the day from weather forecast websites. Using this information, an AI model analyzes it to predict the times and places where people are likely to gather. Based on the analysis results, it predicts that demand will increase at specific locations and times and generates dispatch instructions accordingly.

[0404] Terminal role:

[0405] The terminal receives dispatch instructions from the server and presents real-time information to administrators and drivers of the organization managing the transportation services. This information includes locations where demand is expected to be high and recommended dispatch times. Based on this information, administrators issue instructions to efficiently allocate taxis and other modes of transportation.

[0406] User roles:

[0407] As a user, the taxi driver follows instructions from the terminal, travels to the appropriate location and time, and waits for passengers. For example, if server analysis predicts a high demand for transportation around a particular station after an event, the driver will travel to that station in advance to prepare for passenger pickup. In this way, passenger waiting times are reduced, and efficient operation is achieved.

[0408] This system will improve the efficiency of transportation operations, reducing wasted time and fuel consumption. Operators will be able to improve customer satisfaction and operational efficiency by responding quickly and appropriately.

[0409] The following describes the processing flow.

[0410] Step 1:

[0411] The server collects necessary data from event information sites and weather forecast sites on the internet. Using web scraping and APIs, it stores the name, date, time, location, and weather information of local events in a database.

[0412] Step 2:

[0413] The server inputs the collected event and weather information into an AI model for analysis. The AI ​​model uses a time-series forecasting algorithm to identify times and locations where demand is likely to be high. For example, it takes into account the time a concert ends and changes in the weather throughout the day.

[0414] Step 3:

[0415] Based on the analysis results, the server generates dispatch instructions that correspond to the predicted demand. These instructions are set to place taxis in locations where high demand is predicted, within a specific time frame.

[0416] Step 4:

[0417] The terminal displays dispatch instructions received from the server on the taxi company's administrator dashboard in real time. The administrator uses this information to develop the optimal dispatch plan and send specific instructions to the drivers.

[0418] Step 5:

[0419] The taxi driver, acting as the user, heads to the designated time and location based on instructions from the terminal. The driver uses a map application to confirm their route while traveling, in order to efficiently acquire passengers.

[0420] Step 6:

[0421] The server collects dispatch data for the day and feeds it back into the AI ​​model. This result is used to retrain the model and improve the accuracy of future predictions.

[0422] (Example 1)

[0423] 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."

[0424] In the operation of transportation services, real-time demand forecasting and prompt dispatching instructions based on that forecast are essential for efficient dispatching. However, conventional systems have insufficient analysis of event and weather information, making it difficult to make accurate demand forecasts. As a result, this has led to wasted transportation services, increased fuel consumption, and a decline in customer service.

[0425] 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.

[0426] In this invention, the server includes means for collecting event-related information and weather-related information, means for analyzing the event-related information and weather-related information to predict people's movements, and means for performing the analysis based on prompt sentences using the generated AI model. This enables accurate prediction of people's movements and efficient generation of dispatch instructions based on those predictions.

[0427] "Event information" refers to data including the date, time, location, and scale of events and gatherings held in the local area.

[0428] "Weather information" refers to data including weather conditions, probability of precipitation, temperature, wind speed, etc., for a specific region on a daily basis.

[0429] A "generative AI model" is an algorithm or machine learning model designed to analyze specific patterns or trends and make predictions or decisions based on unverified data.

[0430] A "prompt" is a form of instruction or question entered into a generative AI model when performing analysis or generation.

[0431] A "server" is a computer system or data processing device that performs tasks such as data collection, analysis, and instruction generation.

[0432] "Transportation operation instructions" are instructions to optimize the deployment and operation of transportation at a specific location and time.

[0433] "Feedback" refers to the input of information used to improve models and methods based on actual results obtained in relation to the prediction results generated by the system.

[0434] "Real-time geographical location information" refers to information that can instantly update data indicating the current location, and is used for the efficient operation of transportation systems.

[0435] This system analyzes event and weather information to predict people's movements and optimize transportation scheduling. The system primarily consists of servers, terminals, and users.

[0436] Server role:

[0437] The server collects event and weather information via the internet. Specifically, it obtains event schedules from an event information API and weather data such as precipitation probability and temperature from a weather forecast API. Based on this data, the server uses a generative AI model to input prompt messages and make future predictions. The prompt message would be something like, "Based on the event information and weather conditions for this date and time, please predict the demand for transportation." From the generated predictions, the server estimates when and where taxi demand will increase and creates dispatch instructions.

[0438] Terminal role:

[0439] The terminal receives dispatch instructions from the server and provides them to transportation managers and drivers in real time. Along with the dispatch instructions, the terminal displays geographical location information and recommended dispatch times, enabling efficient allocation of transportation.

[0440] User roles:

[0441] A prime example of a user would be a taxi driver. Drivers follow instructions from their terminal and select the optimal route while checking real-time geographical location information to arrive at locations where demand is predicted on time. For example, dispatch instructions based on the scheduled end time of an event allow drivers to wait at the designated location before that time and efficiently pick up passengers.

[0442] By utilizing this specific example, it is possible to reduce people's waiting times and decrease wasted fuel. Through an efficient dispatch process across the entire system, operational efficiency and customer service will be improved.

[0443] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0444] Step 1:

[0445] The server sends requests to event information APIs and weather forecast APIs via the internet to retrieve information about events and weather conditions. Inputs include specific regions and dates, and outputs include the date, time, location, and scale of the event, as well as weather data (weather, probability of precipitation, temperature, etc.). This information is stored in a database on the server and used in subsequent analysis steps.

[0446] Step 2:

[0447] The server generates prompts to be input into the AI ​​model using the collected event and weather information. For example, it might create a prompt such as, "Based on the event information and weather conditions for this date and time, predict the demand for transportation." Based on this prompt, the AI ​​model predicts the flow of people and outputs predictions for the times and locations where demand will be highest.

[0448] Step 3:

[0449] The server analyzes the prediction results obtained from the generated AI model and generates transportation dispatch instructions. Inputs include predicted demand information and real-time geographical location information. The output is materialized as dispatch instructions for a specific location and time, including which driver should go where.

[0450] Step 4:

[0451] The terminal receives dispatch instructions from the server and notifies transportation service managers and drivers in real time. At the same time, the terminal's display screen shows further details such as recommended routes and estimated arrival times. This provides drivers with the information they need to take the most appropriate dispatch actions.

[0452] Step 5:

[0453] The user, a taxi driver, follows dispatch instructions on the terminal and travels to a location where demand is predicted at the specified time. Input data includes geographical information and estimated arrival time from the terminal, and during actual operation, the system efficiently moves using GPS location tracking and navigation functions. The output is improved passenger efficiency for clients and reduced waiting times.

[0454] (Application Example 1)

[0455] 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."

[0456] In recent years, with the advancement of autonomous driving technology, the efficient operation of transportation systems has become a critical issue. In particular, there is a need to accurately predict the locations and times where demand for transportation fluctuates and to optimize operations. However, conventional systems have not adequately considered event information and weather information for efficient dispatching, resulting in problems such as wasted travel and resource consumption. Therefore, the present invention aims to solve these problems and provide an efficient dispatching system using autonomous vehicles.

[0457] 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.

[0458] In this invention, the server includes means for collecting event information and weather information; means for analyzing the event information and weather information to predict the locations and times when people will gather and predict the demand for transportation; means for generating dispatch instructions for autonomous vehicles based on the prediction results; and means for notifying the control system of the autonomous vehicles of the dispatch instructions. This makes it possible to predict the demand for transportation with high accuracy and realize an efficient dispatch plan without waste.

[0459] "Event information" refers to information related to events and gatherings held in a local area, and it has a significant impact on people's movement and gatherings.

[0460] "Weather information" refers to information about the weather on a given day, such as temperature, precipitation, and wind speed, and is a factor that affects people's going out and traveling.

[0461] "Demand for transportation" is an indicator that shows the degree of demand for means of transport such as taxis, buses, and trains at a specific location and time.

[0462] An "autonomous vehicle" refers to a vehicle that can be operated and driven by itself without human intervention using AI technology, and is expected to be an efficient means of transportation.

[0463] "Dispatch instructions" refer to command information that instructs operators or equipment of a means of transport to move to a specific location, and are used to improve operational efficiency.

[0464] A "control system" refers to all hardware and software used to manage and regulate the operation of transportation or related equipment.

[0465] This invention relates to a system that predicts the locations and times when people gather based on event information and weather information, and enables the efficient dispatch of autonomous vehicles.

[0466] The server first collects event and weather information using public databases and information services on the internet. This information is integrated as "event information" and "weather information," and analyzed using a generative AI model. Based on the analysis results, it predicts areas and times when pedestrian traffic will increase and estimates the demand for transportation. To perform this process, the server uses a high-performance data processing unit and software specialized for AI processing. Specific software includes libraries suitable for data analysis (for example, Python's Pandas and Scikit-learn).

[0467] The terminal receives dispatch instructions transmitted from the server. In this invention, dispatch instructions are directly notified to the control system of the autonomous vehicle, eliminating the need for human intervention as in conventional systems. The terminal can transmit real-time geographical location information and travel route to the traffic system, enabling the autonomous vehicle to efficiently move to locations where people gather and prepare to pick up passengers.

[0468] Users, particularly operators and providers of autonomous vehicles, can use these automated systems to improve vehicle operational efficiency and make more effective use of resources. For example, if a server predicts increased demand around a specific concert venue on a weekend night based on data, autonomous vehicles can wait in that area in advance to smoothly alleviate congestion after the event ends.

[0469] Examples of prompt statements include the following:

[0470] "Based on the following data, predict peak demand and generate the optimal ride-hailing plan: Event information: {event_info}, Weather information: {weather_info}"

[0471] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0472] Step 1:

[0473] The server collects event and weather information from the internet. Specifically, it sends requests to public databases and information websites and retrieves the data obtained as responses. It receives responses from various APIs as input and outputs them as JSON data.

[0474] Step 2:

[0475] The server analyzes the collected event and weather information using an AI model. This analysis preprocesses the information and uses statistical methods and machine learning algorithms to predict the times and places where people are likely to gather. The input is event and weather information in JSON format, which is converted into feature data and input into the AI ​​model, generating a prediction of human flow as output.

[0476] Step 3:

[0477] The server generates dispatch instructions for autonomous vehicles based on the prediction results. In this step, it identifies locations and times when demand for transportation will be high based on the prediction results, and creates the optimal travel route and arrival time. It receives the prediction results of the AI ​​model as input and generates dispatch instruction data as output.

[0478] Step 4:

[0479] The server notifies the terminal of the generated dispatch instructions. The terminal forwards the dispatch instructions received from the server to the control system of the autonomous vehicle, which adjusts the vehicle's location and planned route in real time. The input is dispatch instruction data, and the output is instructions to the control system.

[0480] Step 5:

[0481] Users monitor the operational status of autonomous vehicles via a terminal. Operators and service providers refer to the geographical location information and vehicle status provided on the terminal and adjust operational plans as needed. Input is real-time information from the terminal, and output is to support the operational manager's decision-making.

[0482] 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.

[0483] This invention combines a system for efficiently dispatching transportation services with an emotion engine that recognizes user emotions. This makes it possible to provide personalized services tailored to the user's emotional state.

[0484] Server role:

[0485] The server collects event and weather information in real time and analyzes it to predict where and when people will gather. Furthermore, an emotion engine collects user emotion data and incorporates the analysis results into the forecasting of transportation demand. For example, if a user is excited during an event, this emotion data is taken into account to predict the times when services are most likely to be needed.

[0486] Terminal role:

[0487] Dispatch instructions sent from the server and emotion-based, tailored service information are displayed on a dashboard for transportation operators. Operators can take actions to improve the experience by focusing on specific user groups, such as providing faster taxi services to users with high stress levels.

[0488] User roles:

[0489] Through an interface provided by the emotion engine, users' emotional states are reflected in the service. Drivers receive suggestions to choose specific driving methods based on the user's emotional state. For example, a user feeling anxious can be offered relaxing music or lighting in the car.

[0490] This system allows transportation operators to provide more precise service by utilizing unique emotional data in addition to conventional dispatch efficiency. This will enable them to improve the passenger experience and increase customer satisfaction.

[0491] The following describes the processing flow.

[0492] Step 1:

[0493] The server collects event and weather information from the internet. It uses web scraping or APIs to retrieve local event schedules and weather forecasts, and stores them in a database.

[0494] Step 2:

[0495] The server collects data to recognize the user's emotions. Using an emotion engine, it retrieves emotional data from the user's mobile app or wearable device. This includes heart rate, voice tone, and facial expressions.

[0496] Step 3:

[0497] The server analyzes collected event information, weather information, and sentiment data. This analysis identifies times and locations where people are likely to gather, and adjusts service content to reflect the emotional state of users. For example, if it determines that many users are feeling stressed towards the end of an event, it recommends prompt vehicle dispatch.

[0498] Step 4:

[0499] The server notifies transportation operators of generated dispatch instructions and emotion-based service instructions. This information includes dispatch planning for specific locations and methods to improve the user experience.

[0500] Step 5:

[0501] The terminal displays instructions from the server on an operator's dashboard. Based on this information, operators can adjust dispatch plans and service delivery in real time. For example, they can adjust the temperature and music inside the vehicle before passengers board.

[0502] Step 6:

[0503] Users are configured to receive services based on their emotional state. Taxi drivers review the user's recommended services and customize the experience provided during the ride. For example, the driver might play relaxing music or choose a route that suits the user's preferences.

[0504] Step 7:

[0505] The server collects dispatch results and user feedback for the day. Based on this data, the AI ​​model and emotion engine are continuously improved to enhance the accuracy of future services.

[0506] (Example 2)

[0507] 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."

[0508] The efficiency and accuracy of transportation dispatching currently depend on existing algorithms and data analysis methods, making it difficult to adequately respond to real-time, changing demand. Furthermore, conventional systems cannot provide services that take into account users' emotional states, making it difficult to increase user satisfaction. Therefore, it is necessary to solve these problems by more accurately predicting transportation demand and providing services that respond to users' emotional states.

[0509] 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.

[0510] In this invention, the server includes means for collecting event information and weather information, means for analyzing the event information and weather information to predict the locations and times when people gather, and means for collecting and quantifying the emotional state of users as data. This enables efficient dispatching of transportation and the provision of services tailored to the individual emotions of users.

[0511] "Event information" refers to data about events and gatherings held at specific locations and times, and is used to predict crowd sizes in the ride-hailing system.

[0512] "Weather information" refers to data on current or predicted weather conditions, such as temperature, precipitation, and wind speed, which are considered in order to forecast the demand for transportation.

[0513] "User emotional state" refers to information that indicates the psychological or emotional state an individual user experiences at a specific time, and can be used to personalize service delivery.

[0514] "Dispatch instructions" are instructions regarding the arrangement of transportation methods, generated based on predicted and analyzed data, and are notified to the operator.

[0515] "Notifying operators" means delivering generated instructions and information to transportation managers and drivers, which is necessary for providing appropriate ride-hailing services.

[0516] "Continuously improving analytical methods" means regularly reviewing the system's algorithms and processes based on past predictions and results, and continuously improving its performance.

[0517] "Real-time geographical location information" refers to information that indicates the current location of transportation methods and users, enabling a rapid response in ride-hailing and service provision.

[0518] "Service suggestion" refers to the act of providing special services or suggestions tailored to the user's current emotional state, and is used to improve the user experience.

[0519] This invention provides a more personalized service by combining a transportation dispatch system with technology that recognizes and utilizes user emotions. The server collects event and weather information in real time via a communication network. Data collection utilizes data sources accessible via the internet, and the collected data is stored in databases such as MySQL or PostgreSQL. This information is analyzed using Apache Hadoop and the Python Pandas library to predict where and when people will gather.

[0520] The server also uses an emotion engine to collect user emotional state data. This emotion engine receives user input via a smartphone app or web app, quantifies that input data, and stores it. For example, if a user inputs "relaxed," their emotional state is stored as the number "70."

[0521] The terminal displays dispatch instructions and service guidance based on sentiment data sent from the server on a dashboard for transportation operators. This dashboard is used by operators to take actions focused on specific user groups. Technically, JavaScript libraries such as React and Vue.js are used to create the dashboard.

[0522] Users input their emotional state using an interface provided through an emotion engine, and customized services are then offered. Drivers receive suggestions based on the user's emotions and adjust the in-car environment accordingly. For example, by prompting the generative AI model with "What kind of in-car environment do you want when you want to relax?", they can receive advice on soft music and lighting settings.

[0523] This allows transportation operators to provide accurate dispatching along with services tailored to user needs, thereby improving passenger experience and customer satisfaction.

[0524] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0525] Step 1:

[0526] The server retrieves event and weather information via the internet. It uses APIs and scraping techniques to store the retrieved information in a database. Input is data from a Web API, and output is raw event and weather data stored in the database.

[0527] Step 2:

[0528] The server analyzes collected event and weather information. It uses the Python Pandas library to clean and format the data, then applies an analysis algorithm to predict where and when people will gather. The input to this analysis is the formatted data, and the output is the future locations and times of human congestion derived from the prediction model.

[0529] Step 3:

[0530] The server uses an emotion engine to collect and quantify emotional data from users. The emotional state transmitted by the user through the app or web interface serves as input, which is then output and stored as numerical data. This process objectifies the user's subjective emotional input.

[0531] Step 4:

[0532] The server integrates prediction results and sentiment data to generate dispatch instructions for transportation. Using a generation AI model, it calculates the appropriate number of vehicles and timing, and notifies operators of these instructions. The input is integrated data, and the output is a specific dispatch plan and suggested time slots.

[0533] Step 5:

[0534] The terminal displays dispatch instructions received from the server and sentiment-based service information on a dashboard. This information is visualized using frontend technologies such as React and Vue.js. The input is the received dataset, and the output is an administration screen for operators.

[0535] Step 6:

[0536] The user receives services tailored to their emotional state through a provided interface. At this stage, the driver provides specific services based on instructions received from the server. As an example of a prompt to the generative AI model, the user might input, "Please tell me what kind of in-car environment would make you feel relaxed," and the system would provide an appropriate environment based on the response. The input is the user's emotional data, and the output is a real-time, customized service based on that data.

[0537] (Application Example 2)

[0538] 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."

[0539] Traditional ride-hailing systems for transportation services relied solely on geographical information and time-of-day demand forecasts, without considering the user's emotional state. This often overlooked factors influencing user satisfaction and the quality of their experience, hindering improvements in satisfaction. Furthermore, providing personalized services to individual users proved difficult.

[0540] 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.

[0541] In this invention, the server includes means for collecting event information and weather information, means for analyzing the event information and weather information to predict the locations and times when people gather, and means for recognizing the emotional state of the user and using the emotional data to predict the demand for transportation. This makes it possible to provide a personalized ride-hailing service that responds to the user's emotions.

[0542] "Event information" refers to data about events and activities held at a specific date, time, and location.

[0543] "Weather information" refers to data related to weather conditions, such as temperature, precipitation, and wind speed.

[0544] "User emotional state" refers to data that reflects an individual's psychological and emotional state.

[0545] "Demand forecasting" is the process of performing analysis and calculations to predict future demand and needs.

[0546] "Vehicle dispatch instructions" are instructions given to operators to determine the location and travel time of the means of transport.

[0547] "Transportation" refers to vehicles and transportation systems used for transporting individuals or goods.

[0548] "Service information" refers to information about actions and features provided to improve the user experience.

[0549] A "server" is a networked computer system used for processing, analyzing, and storing data.

[0550] An "emotion engine" is a technology or algorithm used to detect and analyze a user's emotional state.

[0551] The server collects event and weather information in real time, analyzes it, and predicts the places and times when people are likely to gather. Furthermore, it uses an emotion engine to collect users' emotional states in real time and incorporates this into demand forecasting. The server fuses this data to generate optimal dispatch instructions. By using cloud platforms such as Google Cloud and AWS, it is possible to process and analyze large amounts of data. In addition, the emotion engine can use an algorithm developed in-house.

[0552] The terminal displays dispatch instructions sent from the server and service information tailored based on the user's emotions. Through the dashboard, the operator can take actions to provide services that match the user's emotional state (e.g., relaxing music or lighting). Typical terminals used by operators include general-purpose computers and tablets.

[0553] Users communicate their emotional state to the emotion engine via their smartphones, resulting in optimized ride-hailing services. The smartphones are equipped with the Emotion SDK, which detects emotions in real time. Furthermore, the autonomous vehicle interface provides services that reflect this emotional information.

[0554] As a concrete example, when a user calls a taxi after attending an evening event, the server analyzes the number of event attendees and related weather information to predict when taxi demand will be highest. Simultaneously, it retrieves emotional data from the user's smartphone, and if it determines that the user needs to relax, it adjusts the music and environment in the car. This allows the user to travel to their destination comfortably.

[0555] Using a generative AI model, prompts such as "This user is feeling anxious. How should you adjust the in-car environment?" or "Based on the user's emotional data, please select the most relaxing route" are generated based on the user's emotional data.

[0556] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0557] Step 1:

[0558] The server collects event and weather information. Event schedules and weather data are provided as input, and this information is stored in a database in real time. The output is digital data of the collected event and weather information. In this step, the information is automatically retrieved via a web API.

[0559] Step 2:

[0560] The server analyzes collected event and weather information to predict where and when people will gather. It uses the information from the previous step as input and applies an analysis algorithm. The output generates predicted crowd levels for specific time periods and geographical locations. Machine learning algorithms are used for processing this data.

[0561] Step 3:

[0562] The server recognizes the user's emotional state and collects data on it. Input consists of physiological and audio data transmitted from the user's smartphone. Output is analytical data related to the user's emotional state. This data is processed by the emotion engine using the Emotion SDK.

[0563] Step 4:

[0564] The server predicts demand for transportation using customer acquisition forecast data and sentiment data. This includes forecast data and sentiment data obtained from steps 2 and 3 as input data, and optimized demand forecast data is generated as output. In this step, the generated AI model is utilized to integrate demand forecasting and sentiment analysis.

[0565] Step 5:

[0566] The server generates dispatch instructions and notifies operators. Demand forecast data is used as input. Output includes specific dispatch instructions sent to drivers and service recommendations based on sentiment data. At this stage, information is sent to the operator's dashboard via a notification system.

[0567] Step 6:

[0568] Users receive individually optimized ride-hailing services via their smartphones. Input consists of service adjustment information based on prompts. Output is a personalized ride-hailing experience. This process allows users to travel more comfortably and efficiently.

[0569] 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.

[0570] 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.

[0571] 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.

[0572] [Fourth Embodiment]

[0573] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0574] 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.

[0575] 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).

[0576] 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.

[0577] 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.

[0578] 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).

[0579] 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.

[0580] 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.

[0581] 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.

[0582] 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.

[0583] 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.

[0584] 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.

[0585] 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".

[0586] This invention provides a system that predicts the flow of people necessary for the effective operation of transportation and dispatches appropriate vehicles based on that prediction. The system consists of a server, terminals, and users.

[0587] Server role:

[0588] The server collects event and weather information from the internet. For example, it obtains concert schedules for the area and the probability of precipitation for the day from weather forecast websites. Using this information, an AI model analyzes it to predict the times and places where people are likely to gather. Based on the analysis results, it predicts that demand will increase at specific locations and times and generates dispatch instructions accordingly.

[0589] Terminal role:

[0590] The terminal receives dispatch instructions from the server and presents real-time information to administrators and drivers of the organization managing the transportation services. This information includes locations where demand is expected to be high and recommended dispatch times. Based on this information, administrators issue instructions to efficiently allocate taxis and other modes of transportation.

[0591] User roles:

[0592] As a user, the taxi driver follows instructions from the terminal, travels to the appropriate location and time, and waits for passengers. For example, if server analysis predicts a high demand for transportation around a particular station after an event, the driver will travel to that station in advance to prepare for passenger pickup. In this way, passenger waiting times are reduced, and efficient operation is achieved.

[0593] This system will improve the efficiency of transportation operations, reducing wasted time and fuel consumption. Operators will be able to improve customer satisfaction and operational efficiency by responding quickly and appropriately.

[0594] The following describes the processing flow.

[0595] Step 1:

[0596] The server collects necessary data from event information sites and weather forecast sites on the internet. Using web scraping and APIs, it stores the name, date, time, location, and weather information of local events in a database.

[0597] Step 2:

[0598] The server inputs the collected event and weather information into an AI model for analysis. The AI ​​model uses a time-series forecasting algorithm to identify times and locations where demand is likely to be high. For example, it takes into account the time a concert ends and changes in the weather throughout the day.

[0599] Step 3:

[0600] Based on the analysis results, the server generates dispatch instructions that correspond to the predicted demand. These instructions are set to place taxis in locations where high demand is predicted, within a specific time frame.

[0601] Step 4:

[0602] The terminal displays dispatch instructions received from the server on the taxi company's administrator dashboard in real time. The administrator uses this information to develop the optimal dispatch plan and send specific instructions to the drivers.

[0603] Step 5:

[0604] The taxi driver, acting as the user, heads to the designated time and location based on instructions from the terminal. The driver uses a map application to confirm their route while traveling, in order to efficiently acquire passengers.

[0605] Step 6:

[0606] The server collects dispatch data for the day and feeds it back into the AI ​​model. This result is used to retrain the model and improve the accuracy of future predictions.

[0607] (Example 1)

[0608] 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".

[0609] In the operation of transportation services, real-time demand forecasting and prompt dispatching instructions based on that forecast are essential for efficient dispatching. However, conventional systems have insufficient analysis of event and weather information, making it difficult to make accurate demand forecasts. As a result, this has led to wasted transportation services, increased fuel consumption, and a decline in customer service.

[0610] 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.

[0611] In this invention, the server includes means for collecting event-related information and weather-related information, means for analyzing the event-related information and weather-related information to predict people's movements, and means for performing the analysis based on prompt sentences using the generated AI model. This enables accurate prediction of people's movements and efficient generation of dispatch instructions based on those predictions.

[0612] "Event information" refers to data including the date, time, location, and scale of events and gatherings held in the local area.

[0613] "Weather information" refers to data including weather conditions, probability of precipitation, temperature, wind speed, etc., for a specific region on a daily basis.

[0614] A "generative AI model" is an algorithm or machine learning model designed to analyze specific patterns or trends and make predictions or decisions based on unverified data.

[0615] A "prompt" is a form of instruction or question entered into a generative AI model when performing analysis or generation.

[0616] A "server" is a computer system or data processing device that performs tasks such as data collection, analysis, and instruction generation.

[0617] "Transportation operation instructions" are instructions to optimize the deployment and operation of transportation at a specific location and time.

[0618] "Feedback" refers to the input of information used to improve models and methods based on actual results obtained in relation to the prediction results generated by the system.

[0619] "Real-time geographical location information" refers to information that can instantly update data indicating the current location, and is used for the efficient operation of transportation systems.

[0620] This system analyzes event and weather information to predict people's movements and optimize transportation scheduling. The system primarily consists of servers, terminals, and users.

[0621] Server role:

[0622] The server collects event and weather information via the internet. Specifically, it obtains event schedules from an event information API and weather data such as precipitation probability and temperature from a weather forecast API. Based on this data, the server uses a generative AI model to input prompt messages and make future predictions. The prompt message would be something like, "Based on the event information and weather conditions for this date and time, please predict the demand for transportation." From the generated predictions, the server estimates when and where taxi demand will increase and creates dispatch instructions.

[0623] Terminal role:

[0624] The terminal receives dispatch instructions from the server and provides them to transportation managers and drivers in real time. Along with the dispatch instructions, the terminal displays geographical location information and recommended dispatch times, enabling efficient allocation of transportation.

[0625] User roles:

[0626] A prime example of a user would be a taxi driver. Drivers follow instructions from their terminal and select the optimal route while checking real-time geographical location information to arrive at locations where demand is predicted on time. For example, dispatch instructions based on the scheduled end time of an event allow drivers to wait at the designated location before that time and efficiently pick up passengers.

[0627] By utilizing this specific example, it is possible to reduce people's waiting times and decrease wasted fuel. Through an efficient dispatch process across the entire system, operational efficiency and customer service will be improved.

[0628] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0629] Step 1:

[0630] The server sends requests to event information APIs and weather forecast APIs via the internet to retrieve information about events and weather conditions. Inputs include specific regions and dates, and outputs include the date, time, location, and scale of the event, as well as weather data (weather, probability of precipitation, temperature, etc.). This information is stored in a database on the server and used in subsequent analysis steps.

[0631] Step 2:

[0632] The server generates prompts to be input into the AI ​​model using the collected event and weather information. For example, it might create a prompt such as, "Based on the event information and weather conditions for this date and time, predict the demand for transportation." Based on this prompt, the AI ​​model predicts the flow of people and outputs predictions for the times and locations where demand will be highest.

[0633] Step 3:

[0634] The server analyzes the prediction results obtained from the generated AI model and generates transportation dispatch instructions. Inputs include predicted demand information and real-time geographical location information. The output is materialized as dispatch instructions for a specific location and time, including which driver should go where.

[0635] Step 4:

[0636] The terminal receives dispatch instructions from the server and notifies transportation service managers and drivers in real time. At the same time, the terminal's display screen shows further details such as recommended routes and estimated arrival times. This provides drivers with the information they need to take the most appropriate dispatch actions.

[0637] Step 5:

[0638] The user, a taxi driver, follows dispatch instructions on the terminal and travels to a location where demand is predicted at the specified time. Input data includes geographical information and estimated arrival time from the terminal, and during actual operation, the system efficiently moves using GPS location tracking and navigation functions. The output is improved passenger efficiency for clients and reduced waiting times.

[0639] (Application Example 1)

[0640] 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".

[0641] In recent years, with the advancement of autonomous driving technology, the efficient operation of transportation systems has become a critical issue. In particular, there is a need to accurately predict the locations and times where demand for transportation fluctuates and to optimize operations. However, conventional systems have not adequately considered event information and weather information for efficient dispatching, resulting in problems such as wasted travel and resource consumption. Therefore, the present invention aims to solve these problems and provide an efficient dispatching system using autonomous vehicles.

[0642] 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.

[0643] In this invention, the server includes means for collecting event information and weather information; means for analyzing the event information and weather information to predict the locations and times when people will gather and predict the demand for transportation; means for generating dispatch instructions for autonomous vehicles based on the prediction results; and means for notifying the control system of the autonomous vehicles of the dispatch instructions. This makes it possible to predict the demand for transportation with high accuracy and realize an efficient dispatch plan without waste.

[0644] "Event information" refers to information related to events and gatherings held in a local area, and it has a significant impact on people's movement and gatherings.

[0645] "Weather information" refers to information about the weather on a given day, such as temperature, precipitation, and wind speed, and is a factor that affects people's going out and traveling.

[0646] "Demand for transportation" is an indicator that shows the degree of demand for means of transport such as taxis, buses, and trains at a specific location and time.

[0647] An "autonomous vehicle" refers to a vehicle that can be operated and driven by itself without human intervention using AI technology, and is expected to be an efficient means of transportation.

[0648] "Dispatch instructions" refer to command information that instructs operators or equipment of a means of transport to move to a specific location, and are used to improve operational efficiency.

[0649] A "control system" refers to all hardware and software used to manage and regulate the operation of transportation or related equipment.

[0650] This invention relates to a system that predicts the locations and times when people gather based on event information and weather information, and enables the efficient dispatch of autonomous vehicles.

[0651] The server first collects event and weather information using public databases and information services on the internet. This information is integrated as "event information" and "weather information," and analyzed using a generative AI model. Based on the analysis results, it predicts areas and times when pedestrian traffic will increase and estimates the demand for transportation. To perform this process, the server uses a high-performance data processing unit and software specialized for AI processing. Specific software includes libraries suitable for data analysis (for example, Python's Pandas and Scikit-learn).

[0652] The terminal receives dispatch instructions transmitted from the server. In this invention, dispatch instructions are directly notified to the control system of the autonomous vehicle, eliminating the need for human intervention as in conventional systems. The terminal can transmit real-time geographical location information and travel route to the traffic system, enabling the autonomous vehicle to efficiently move to locations where people gather and prepare to pick up passengers.

[0653] Users, particularly operators and providers of autonomous vehicles, can use these automated systems to improve vehicle operational efficiency and make more effective use of resources. For example, if a server predicts increased demand around a specific concert venue on a weekend night based on data, autonomous vehicles can wait in that area in advance to smoothly alleviate congestion after the event ends.

[0654] Examples of prompt statements include the following:

[0655] "Based on the following data, predict peak demand and generate the optimal ride-hailing plan: Event information: {event_info}, Weather information: {weather_info}"

[0656] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0657] Step 1:

[0658] The server collects event and weather information from the internet. Specifically, it sends requests to public databases and information websites and retrieves the data obtained as responses. It receives responses from various APIs as input and outputs them as JSON data.

[0659] Step 2:

[0660] The server analyzes the collected event and weather information using an AI model. This analysis preprocesses the information and uses statistical methods and machine learning algorithms to predict the times and places where people are likely to gather. The input is event and weather information in JSON format, which is converted into feature data and input into the AI ​​model, generating a prediction of human flow as output.

[0661] Step 3:

[0662] The server generates dispatch instructions for autonomous vehicles based on the prediction results. In this step, it identifies locations and times when demand for transportation will be high based on the prediction results, and creates the optimal travel route and arrival time. It receives the prediction results of the AI ​​model as input and generates dispatch instruction data as output.

[0663] Step 4:

[0664] The server notifies the terminal of the generated dispatch instructions. The terminal forwards the dispatch instructions received from the server to the control system of the autonomous vehicle, which adjusts the vehicle's location and planned route in real time. The input is dispatch instruction data, and the output is instructions to the control system.

[0665] Step 5:

[0666] Users monitor the operational status of autonomous vehicles via a terminal. Operators and service providers refer to the geographical location information and vehicle status provided on the terminal and adjust operational plans as needed. Input is real-time information from the terminal, and output is to support the operational manager's decision-making.

[0667] 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.

[0668] This invention combines a system for efficiently dispatching transportation services with an emotion engine that recognizes user emotions. This makes it possible to provide personalized services tailored to the user's emotional state.

[0669] Server role:

[0670] The server collects event and weather information in real time and analyzes it to predict where and when people will gather. Furthermore, an emotion engine collects user emotion data and incorporates the analysis results into the forecasting of transportation demand. For example, if a user is excited during an event, this emotion data is taken into account to predict the times when services are most likely to be needed.

[0671] Terminal role:

[0672] Dispatch instructions sent from the server and emotion-based, tailored service information are displayed on a dashboard for transportation operators. Operators can take actions to improve the experience by focusing on specific user groups, such as providing faster taxi services to users with high stress levels.

[0673] User roles:

[0674] Through an interface provided by the emotion engine, users' emotional states are reflected in the service. Drivers receive suggestions to choose specific driving methods based on the user's emotional state. For example, a user feeling anxious can be offered relaxing music or lighting in the car.

[0675] This system allows transportation operators to provide more precise service by utilizing unique emotional data in addition to conventional dispatch efficiency. This will enable them to improve the passenger experience and increase customer satisfaction.

[0676] The following describes the processing flow.

[0677] Step 1:

[0678] The server collects event and weather information from the internet. It uses web scraping or APIs to retrieve local event schedules and weather forecasts, and stores them in a database.

[0679] Step 2:

[0680] The server collects data to recognize the user's emotions. Using an emotion engine, it retrieves emotional data from the user's mobile app or wearable device. This includes heart rate, voice tone, and facial expressions.

[0681] Step 3:

[0682] The server analyzes collected event information, weather information, and sentiment data. This analysis identifies times and locations where people are likely to gather, and adjusts service content to reflect the emotional state of users. For example, if it determines that many users are feeling stressed towards the end of an event, it recommends prompt vehicle dispatch.

[0683] Step 4:

[0684] The server notifies transportation operators of generated dispatch instructions and emotion-based service instructions. This information includes dispatch planning for specific locations and methods to improve the user experience.

[0685] Step 5:

[0686] The terminal displays instructions from the server on an operator's dashboard. Based on this information, operators can adjust dispatch plans and service delivery in real time. For example, they can adjust the temperature and music inside the vehicle before passengers board.

[0687] Step 6:

[0688] Users are configured to receive services based on their emotional state. Taxi drivers review the user's recommended services and customize the experience provided during the ride. For example, the driver might play relaxing music or choose a route that suits the user's preferences.

[0689] Step 7:

[0690] The server collects dispatch results and user feedback for the day. Based on this data, the AI ​​model and emotion engine are continuously improved to enhance the accuracy of future services.

[0691] (Example 2)

[0692] 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".

[0693] The efficiency and accuracy of transportation dispatching currently depend on existing algorithms and data analysis methods, making it difficult to adequately respond to real-time, changing demand. Furthermore, conventional systems cannot provide services that take into account users' emotional states, making it difficult to increase user satisfaction. Therefore, it is necessary to solve these problems by more accurately predicting transportation demand and providing services that respond to users' emotional states.

[0694] 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.

[0695] In this invention, the server includes means for collecting event information and weather information, means for analyzing the event information and weather information to predict the locations and times when people gather, and means for collecting and quantifying the emotional state of users as data. This enables efficient dispatching of transportation and the provision of services tailored to the individual emotions of users.

[0696] "Event information" refers to data about events and gatherings held at specific locations and times, and is used to predict crowd sizes in the ride-hailing system.

[0697] "Weather information" refers to data on current or predicted weather conditions, such as temperature, precipitation, and wind speed, which are considered in order to forecast the demand for transportation.

[0698] "User emotional state" refers to information that indicates the psychological or emotional state an individual user experiences at a specific time, and can be used to personalize service delivery.

[0699] "Dispatch instructions" are instructions regarding the arrangement of transportation methods, generated based on predicted and analyzed data, and are notified to the operator.

[0700] "Notifying operators" means delivering generated instructions and information to transportation managers and drivers, which is necessary for providing appropriate ride-hailing services.

[0701] "Continuously improving analytical methods" means regularly reviewing the system's algorithms and processes based on past predictions and results, and continuously improving its performance.

[0702] "Real-time geographical location information" refers to information that indicates the current location of transportation methods and users, enabling a rapid response in ride-hailing and service provision.

[0703] "Service suggestion" refers to the act of providing special services or suggestions tailored to the user's current emotional state, and is used to improve the user experience.

[0704] This invention provides a more personalized service by combining a transportation dispatch system with technology that recognizes and utilizes user emotions. The server collects event and weather information in real time via a communication network. Data collection utilizes data sources accessible via the internet, and the collected data is stored in databases such as MySQL or PostgreSQL. This information is analyzed using Apache Hadoop and the Python Pandas library to predict where and when people will gather.

[0705] The server also uses an emotion engine to collect user emotional state data. This emotion engine receives user input via a smartphone app or web app, quantifies that input data, and stores it. For example, if a user inputs "relaxed," their emotional state is stored as the number "70."

[0706] The terminal displays dispatch instructions and service guidance based on sentiment data sent from the server on a dashboard for transportation operators. This dashboard is used by operators to take actions focused on specific user groups. Technically, JavaScript libraries such as React and Vue.js are used to create the dashboard.

[0707] Users input their emotional state using an interface provided through an emotion engine, and customized services are then offered. Drivers receive suggestions based on the user's emotions and adjust the in-car environment accordingly. For example, by prompting the generative AI model with "What kind of in-car environment do you want when you want to relax?", they can receive advice on soft music and lighting settings.

[0708] This allows transportation operators to provide accurate dispatching along with services tailored to user needs, thereby improving passenger experience and customer satisfaction.

[0709] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0710] Step 1:

[0711] The server retrieves event and weather information via the internet. It uses APIs and scraping techniques to store the retrieved information in a database. Input is data from a Web API, and output is raw event and weather data stored in the database.

[0712] Step 2:

[0713] The server analyzes collected event and weather information. It uses the Python Pandas library to clean and format the data, then applies an analysis algorithm to predict where and when people will gather. The input to this analysis is the formatted data, and the output is the future locations and times of human congestion derived from the prediction model.

[0714] Step 3:

[0715] The server uses an emotion engine to collect and quantify emotional data from users. The emotional state transmitted by the user through the app or web interface serves as input, which is then output and stored as numerical data. This process objectifies the user's subjective emotional input.

[0716] Step 4:

[0717] The server integrates prediction results and sentiment data to generate dispatch instructions for transportation. Using a generation AI model, it calculates the appropriate number of vehicles and timing, and notifies operators of these instructions. The input is integrated data, and the output is a specific dispatch plan and suggested time slots.

[0718] Step 5:

[0719] The terminal displays dispatch instructions received from the server and sentiment-based service information on a dashboard. This information is visualized using frontend technologies such as React and Vue.js. The input is the received dataset, and the output is an administration screen for operators.

[0720] Step 6:

[0721] The user receives services tailored to their emotional state through a provided interface. At this stage, the driver provides specific services based on instructions received from the server. As an example of a prompt to the generative AI model, the user might input, "Please tell me what kind of in-car environment would make you feel relaxed," and the system would provide an appropriate environment based on the response. The input is the user's emotional data, and the output is a real-time, customized service based on that data.

[0722] (Application Example 2)

[0723] 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".

[0724] Traditional ride-hailing systems for transportation services relied solely on geographical information and time-of-day demand forecasts, without considering the user's emotional state. This often overlooked factors influencing user satisfaction and the quality of their experience, hindering improvements in satisfaction. Furthermore, providing personalized services to individual users proved difficult.

[0725] 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.

[0726] In this invention, the server includes means for collecting event information and weather information, means for analyzing the event information and weather information to predict the locations and times when people gather, and means for recognizing the emotional state of the user and using the emotional data to predict the demand for transportation. This makes it possible to provide a personalized ride-hailing service that responds to the user's emotions.

[0727] "Event information" refers to data about events and activities held at a specific date, time, and location.

[0728] "Weather information" refers to data related to weather conditions, such as temperature, precipitation, and wind speed.

[0729] "User emotional state" refers to data that reflects an individual's psychological and emotional state.

[0730] "Demand forecasting" is the process of performing analysis and calculations to predict future demand and needs.

[0731] "Vehicle dispatch instructions" are instructions given to operators to determine the location and travel time of the means of transport.

[0732] "Transportation" refers to vehicles and transportation systems used for transporting individuals or goods.

[0733] "Service information" refers to information about actions and features provided to improve the user experience.

[0734] A "server" is a networked computer system used for processing, analyzing, and storing data.

[0735] An "emotion engine" is a technology or algorithm used to detect and analyze a user's emotional state.

[0736] The server collects event and weather information in real time, analyzes it, and predicts the places and times when people are likely to gather. Furthermore, it uses an emotion engine to collect users' emotional states in real time and incorporates this into demand forecasting. The server fuses this data to generate optimal dispatch instructions. By using cloud platforms such as Google Cloud and AWS, it is possible to process and analyze large amounts of data. In addition, the emotion engine can use an algorithm developed in-house.

[0737] The terminal displays dispatch instructions sent from the server and service information tailored based on the user's emotions. Through the dashboard, the operator can take actions to provide services that match the user's emotional state (e.g., relaxing music or lighting). Typical terminals used by operators include general-purpose computers and tablets.

[0738] Users communicate their emotional state to the emotion engine via their smartphones, resulting in optimized ride-hailing services. The smartphones are equipped with the Emotion SDK, which detects emotions in real time. Furthermore, the autonomous vehicle interface provides services that reflect this emotional information.

[0739] As a concrete example, when a user calls a taxi after attending an evening event, the server analyzes the number of event attendees and related weather information to predict when taxi demand will be highest. Simultaneously, it retrieves emotional data from the user's smartphone, and if it determines that the user needs to relax, it adjusts the music and environment in the car. This allows the user to travel to their destination comfortably.

[0740] Using a generative AI model, prompts such as "This user is feeling anxious. How should you adjust the in-car environment?" or "Based on the user's emotional data, please select the most relaxing route" are generated based on the user's emotional data.

[0741] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0742] Step 1:

[0743] The server collects event and weather information. Event schedules and weather data are provided as input, and this information is stored in a database in real time. The output is digital data of the collected event and weather information. In this step, the information is automatically retrieved via a web API.

[0744] Step 2:

[0745] The server analyzes collected event and weather information to predict where and when people will gather. It uses the information from the previous step as input and applies an analysis algorithm. The output generates predicted crowd levels for specific time periods and geographical locations. Machine learning algorithms are used for processing this data.

[0746] Step 3:

[0747] The server recognizes the user's emotional state and collects data on it. Input consists of physiological and audio data transmitted from the user's smartphone. Output is analytical data related to the user's emotional state. This data is processed by the emotion engine using the Emotion SDK.

[0748] Step 4:

[0749] The server predicts demand for transportation using customer acquisition forecast data and sentiment data. This includes forecast data and sentiment data obtained from steps 2 and 3 as input data, and optimized demand forecast data is generated as output. In this step, the generated AI model is utilized to integrate demand forecasting and sentiment analysis.

[0750] Step 5:

[0751] The server generates dispatch instructions and notifies operators. Demand forecast data is used as input. Output includes specific dispatch instructions sent to drivers and service recommendations based on sentiment data. At this stage, information is sent to the operator's dashboard via a notification system.

[0752] Step 6:

[0753] Users receive individually optimized ride-hailing services via their smartphones. Input consists of service adjustment information based on prompts. Output is a personalized ride-hailing experience. This process allows users to travel more comfortably and efficiently.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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."

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0775] The following is further disclosed regarding the embodiments described above.

[0776] (Claim 1)

[0777] Means for collecting event information and weather information,

[0778] A means for predicting the location and time when people will gather by analyzing the aforementioned event information and weather information,

[0779] A means for generating dispatch instructions for means of transport based on the aforementioned prediction results,

[0780] A means for notifying the operator of the means of transport of the aforementioned dispatch instructions,

[0781] A system that includes this.

[0782] (Claim 2)

[0783] The system according to claim 1, further comprising means for continuously improving the analysis means using the prediction results as feedback.

[0784] (Claim 3)

[0785] The system according to claim 1, further comprising means for notifying the operator of the dispatch instruction as information including at least real-time geographic location information.

[0786] "Example 1"

[0787] (Claim 1)

[0788] Means for collecting information about events and weather,

[0789] A means for predicting human movement by analyzing information about the aforementioned event and weather information,

[0790] A means for performing the analysis based on a prompt sentence using the aforementioned generation AI model,

[0791] A means for generating operational instructions for transportation based on analysis results,

[0792] A means for communicating the aforementioned operation instructions to the transportation service manager,

[0793] A system that includes this.

[0794] (Claim 2)

[0795] The system according to claim 1, further comprising means for continuously optimizing the generated AI model by using the analysis results as feedback.

[0796] (Claim 3)

[0797] The system according to claim 1, further comprising means for transmitting the aforementioned operation instructions to an administrator, including at least real-time geographic location information.

[0798] "Application Example 1"

[0799] (Claim 1)

[0800] Means for collecting event information and weather information,

[0801] A means for predicting the locations and times when people will gather by analyzing the aforementioned event information and weather information, and for predicting the demand for transportation methods,

[0802] means for generating dispatch instructions for autonomous vehicles based on the aforementioned prediction results,

[0803] Means for notifying the control system of the autonomous vehicle of the dispatch instruction,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, further comprising means for continuously improving the analysis means using the prediction results as feedback to optimize the vehicle dispatch plan.

[0807] (Claim 3)

[0808] The system according to claim 1, further comprising means for notifying a control system of the dispatch instruction as information including at least real-time geographic location information and the travel route of the autonomous vehicle.

[0809] "Example 2 of combining an emotion engine"

[0810] (Claim 1)

[0811] Means for collecting event information and weather information,

[0812] A means for predicting the location and time when people will gather by analyzing the aforementioned event information and weather information,

[0813] A means of collecting and quantifying the emotional state of users as data,

[0814] A means for generating dispatch instructions for transportation based on the aforementioned prediction results and sentiment data,

[0815] A means of notifying the operator of the transportation service of the aforementioned dispatch instructions and including service suggestions based on sentiment data,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, further comprising means for continuously improving the analysis means using the prediction results and sentiment data as feedback.

[0819] (Claim 3)

[0820] The system according to claim 1, further comprising means for notifying the operator of the dispatch instruction as information including at least real-time geographic location information and service suggestions based on user sentiment.

[0821] "Application example 2 when combining with an emotional engine"

[0822] (Claim 1)

[0823] Means for collecting event information and weather information,

[0824] A means for predicting the location and time when people will gather by analyzing the aforementioned event information and weather information,

[0825] A means of recognizing the emotional state of users and using that emotional data to predict demand for transportation methods,

[0826] A means for generating dispatch instructions for means of transport based on the aforementioned prediction results,

[0827] A means for notifying the operator of the means of transport of the aforementioned dispatch instructions and providing service information based on the user's emotional state,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, further comprising means for continuously improving the analysis means using the prediction results and user sentiment data as feedback.

[0831] (Claim 3)

[0832] The system according to claim 1, further comprising means for notifying the operator of the dispatch instruction as information including at least real-time geographic location information and sentiment-based service information. [Explanation of Symbols]

[0833] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for collecting event information and weather information, A means for predicting the locations and times when people will gather by analyzing the aforementioned event information and weather information, and for predicting the demand for transportation methods, means for generating dispatch instructions for autonomous vehicles based on the aforementioned prediction results, Means for notifying the control system of the autonomous vehicle of the dispatch instruction, A system that includes this.

2. The system according to claim 1, further comprising means for continuously improving the analysis means using the prediction results as feedback to optimize the vehicle dispatch plan.

3. The system according to claim 1, further comprising means for notifying a control system of the dispatch instruction as information including at least real-time geographic location information and the travel route of the autonomous vehicle.

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