system

Abstract

We provide the system. [Solution] A data analysis tool for selecting the optimal mode of transport based on the geographical location and destination of the user of the mode of transport, A learning means for learning the user's preferences and reflecting them in the selection of the means of transport, A demand forecasting means for optimizing the arrangement of the aforementioned transport means, An instruction means for providing instructions to the transport means, A communication means for transmitting the operation information of the aforementioned means of transport to a user terminal in real time and requesting confirmation, The terminal includes a display means that allows the user to visually monitor the operating status, 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The problems in conventional means of transportation are that the means of transportation is selected without considering the preferences and usage history of users, resulting in long waiting times and opaque fares, causing dissatisfaction among users. Also, for the supply side of the means of transportation, it is difficult to appropriately respond to fluctuations in demand, and there is a problem that efficient operation cannot be achieved.

Means for Solving the Problems

[0005] This invention solves this problem by providing a system that selects the optimal mode of transport based on the geographical location and destination of the user of the means of transport. This system acquires the geographical location and destination of the user using a data analysis means, and further incorporates the user's preferences into the selection of the mode of transport using a learning means. In addition, it optimizes the arrangement of the mode of transport using a demand forecasting means and provides instructions to the mode of transport using an instruction means, thereby enabling the provision of efficient means of transport.

[0006] "Means of transportation" is a term that refers to all means of transporting users from one point to another.

[0007] "Users" refers to end-users, such as individuals or groups, who use means of transportation to reach a specific destination.

[0008] "Geographic location" refers to coordinate information of a specific point on Earth.

[0009] "Destination" refers to the final location or place that a user intends to reach using their means of transportation.

[0010] "Transportation means" refers to specific means or devices that constitute a part of the means of transportation and are used to actually move users, such as vehicles.

[0011] "Data analysis means" refers to the technologies, methods, and devices used to collect and analyze various data about users.

[0012] "Learning methods" refer to methods and technologies for learning data about users' past behavior and preferences and incorporating that into their choices.

[0013] "Demand forecasting tools" refer to analytical methods and equipment used to predict future demand for services in a market or specific area and to respond to fluctuations in that demand.

[0014] "Optimizing the configuration" refers to methods and strategies for arranging available resources and transportation means most effectively to maximize efficiency.

[0015] "Instruction means" refers to technologies, methods, or devices for instructing a transportation means to perform specific actions or routes.

Brief Description of the Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Modes for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0019] In the following embodiments, the 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.

[0020] 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.

[0021] 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, etc.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

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

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention provides a system for optimizing means of transportation. The system consists of three main components: a server, a terminal, and a user.

[0038] First, the user enters a ride request using a terminal application. The user can specify their current location and destination, and enter their preferred conditions (for example, a quiet interior or a specific vehicle type). The user's input information is sent to the server in real time.

[0039] The server has data analysis tools designed to analyze geographical location and destination data provided by users, as well as past usage history and preferences. A learning tool on the server records each user's past travel history and learns the patterns. Based on this information, it selects a mode of transportation that matches the user's preferences.

[0040] Furthermore, the server uses demand forecasting tools to analyze current traffic conditions and area demand in real time. Based on this analysis, it determines the optimal allocation of transportation. For example, if demand is high in a particular area, transportation will be concentrated in that area.

[0041] The selected mode of transport and optimized route information are transmitted from the server to the user's terminal. The server also provides detailed operational instructions (such as estimated arrival time and recommended route) to the selected mode of transport using an instruction mechanism.

[0042] The terminal displays information sent from the server to the user and requests confirmation. If the user is satisfied with the presented transportation method and fare information, selecting "Confirm" completes the arrangement of transportation. Through this procedure, users can travel to their destination efficiently without waiting time.

[0043] As a concrete example, suppose a user wants to travel to a meeting within the city and prefers a highly-rated driver and a comfortable ride. In this case, the server analyzes past patterns and current traffic conditions to instantly select and provide a mode of transportation that matches the user's preferences. This process ensures that the user enjoys a satisfying travel experience.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user opens the terminal application and enters their current location, destination, and ride preferences. This information is then sent from the terminal to the server.

[0047] Step 2:

[0048] The server receives information from the user and uses data analysis tools to retrieve the user's past usage history and preferences from the database.

[0049] Step 3:

[0050] The server uses learning mechanisms to select a transportation method that best suits the user's preferences based on user input and past history.

[0051] Step 4:

[0052] The server uses demand forecasting tools to analyze the demand across the entire region in real time and optimize the allocation of selected transportation methods.

[0053] Step 5:

[0054] The server sends the selected transportation method, optimal route, and pre-calculated fare to the user's terminal.

[0055] Step 6:

[0056] The terminal displays information received from the server to the user, and the user confirms the presented information.

[0057] Step 7:

[0058] If the user is satisfied with the proposed transportation method and price, they tap the "Confirm" button. This confirmation is then sent from the device to the server.

[0059] Step 8:

[0060] The server receives user confirmation and sends action instructions (estimated arrival time and optimal route) to the selected means of transport using the instruction mechanism.

[0061] (Example 1)

[0062] 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."

[0063] To achieve efficient use of transportation and improve user satisfaction, accurate selection of transportation methods based on user requests and optimal allocation according to demand are necessary. However, previous systems have struggled to analyze user requests in real time and provide efficient transportation methods. In particular, there has been a problem in that the rapid selection of transportation methods, taking into account traffic conditions and user preferences, has not been adequately performed.

[0064] 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.

[0065] In this invention, the server includes information analysis means, a learning device, and a request prediction device. This enables the selection of the optimal means of transport based on the user's geographical location, destination, and preferences, and the optimal allocation of means of transport according to demand.

[0066] "Information analysis means" refers to technology that analyzes the user's geographical location and destination information to select the appropriate means of transportation.

[0067] A "learning device" is a machine that learns patterns based on the user's past usage history and preferences, and uses this information to select the appropriate means of transportation.

[0068] A "demand forecasting device" is a mechanism that forecasts demand in each region and optimally allocates transportation methods.

[0069] A "communication device" is a device that provides selected means of transport and optimized route information based on information received from the user's terminal.

[0070] An "instruction device" is a machine that provides detailed operational instructions to a selected means of transport.

[0071] This system aims to optimize transportation by effectively managing transportation methods using information analysis tools, learning devices, and request prediction devices. It primarily revolves around three components: a server, terminals, and users, all working together to function.

[0072] The server collects and analyzes geographic location and destination data sent from each user as a means of information analysis. Databases and data analysis software are used for this process. The technologies employed include, for example, Python libraries and analysis algorithms using the R language. Furthermore, a generative AI model is employed to learn from past travel history, utilizing scikit-learn and TENSORFLOW® to thoroughly learn user patterns.

[0073] The terminal provides an interface for user interaction. When a user enters a ride request, the terminal quickly transmits that information to the server. The information transmitted from the terminal is processed by the server in real time, and the results are fed back to the user. This communication utilizes common mobile communication technologies and internet protocols.

[0074] Through a terminal application, users specify their current location, destination, and special requirements (e.g., "quiet interior" or "highly-rated driver"). This allows the system to select the most suitable mode of transportation based on the user's needs. A concrete example is when a user plans to travel to a meeting within the city; the system suggests appropriate taxis or rideshare services.

[0075] For demand forecasting, the server performs real-time traffic analysis and area-specific demand analysis. In some cases, an automated geographic information system (GIS) is used. Based on the obtained data, the server runs a model to most efficiently allocate transportation methods, enabling a rapid response.

[0076] A concrete example of a prompt is the input, "Please suggest the best mode of transportation based on the user's current location, destination, and preferences." This allows the generative AI model to perform appropriate data analysis and provide the most satisfying mode of transportation for the user.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The user enters their ride request using a terminal application. The user enters their current location, destination, and desired conditions (e.g., quiet interior, specific vehicle type). This information is entered through the terminal's UI and prepared as data in JSON format. The terminal then prepares to send this data to the server. The entered information embodies the user's travel request.

[0080] Step 2:

[0081] The terminal transmits JSON-formatted data obtained from the user to the server in real time. This transmission utilizes mobile networks and internet communication, and is conducted via the HTTP protocol. This ensures that user requests are quickly and reliably transmitted to the server. The terminal's output is user request information encoded in a format that the server can parse.

[0082] Step 3:

[0083] The server analyzes the received data. For analysis, the server uses a generative AI model and analyzes user data, past usage history, and current traffic information. Specifically, it uses data analysis tools such as Python and R. The input data is clustered and categorized to determine which mode of transport is optimal. This process generates an output that selects the most suitable mode of transport for the user.

[0084] Step 4:

[0085] The server performs demand forecasting. Real-time geographic and traffic data are used for demand forecasting. The server utilizes GIS tools to predict demand in a specific area and calculates the optimal placement of transportation methods based on that forecast. The input is current geographic and traffic data, and the output is a guideline for transportation placement.

[0086] Step 5:

[0087] The server transmits real-time information to the terminal, including optimal transportation methods and route information. This information includes the selected transportation method, recommended route, and estimated arrival time. The HTTP protocol is again used for this communication, and real-time operation is required. The server then performs a process of providing the selection information to the user as output.

[0088] Step 6:

[0089] The terminal displays information received from the server to the user. The user reviews this information and, if they agree to the proposed transportation method and fee, accepts it by clicking the "Confirm" button. The input is the transportation method information from the server, and the output is the data that is resent to the server as the user's consent or request for correction.

[0090] Step 7:

[0091] The server, upon user confirmation, sends instructions to the transportation provider. These instructions include the planned route, recommended routes, and any special requests. The details are transmitted to the relevant transportation service via the server's communication equipment. As a result of these instructions, the selected transportation provider can quickly head to the user's location.

[0092] (Application Example 1)

[0093] 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."

[0094] In modern society, there is a demand for efficient and customized means of transportation. However, challenges remain in selecting the optimal mode of transport to suit the diverse preferences of each user and providing real-time monitoring of transportation information. Therefore, improving convenience during travel and enhancing the user experience are crucial.

[0095] 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.

[0096] In this invention, the server includes data analysis means for selecting the optimal mode of transport based on the user's geographical location and destination; learning means for learning the user's preferences and reflecting them in the selection of the mode of transport; and communication means for transmitting real-time operational information of the mode of transport to the user terminal and requesting confirmation. As a result, the user can select the optimal mode of transport according to their preferences in real time and visually monitor the operational status on their terminal.

[0097] "Means of transportation" refers to all means of transporting users from one point to another.

[0098] "User" refers to an individual or group that wishes to travel using a means of transportation.

[0099] "Geographic location" refers to specific geographical coordinates obtained using the device's location information function.

[0100] "Destination" refers to the point designated by the user as the final stop of their journey.

[0101] "Transportation means" refers to the specific means of transport or devices used to achieve movement.

[0102] "Data analysis means" refers to devices or software that analyze the user's geographical location and destination information and select the optimal means of transportation.

[0103] "Learning tools" refer to devices or software that accumulate data on a user's past behavior and preferences, and use that data to select the most suitable mode of transportation for that user.

[0104] "Demand forecasting tools" refer to devices or software that analyze past usage patterns of transportation methods and current conditions to predict future demand for transportation methods.

[0105] "Instruction means" refers to devices or software that have the function of providing detailed operational instructions and recommended routes for selected means of transport.

[0106] "Communication means" refers to devices or software that have the function of sending and receiving data bidirectionally between a server and a user's terminal.

[0107] "Display means" refers to devices or software that have the function of visually presenting information to the user on a terminal.

[0108] The system that realizes this invention consists of three main elements: a server, a terminal, and a user. The server is built using programming languages ​​such as Python or Java (registered trademark) and is operated on hardware capable of processing large amounts of data at high speed. Machine learning libraries such as Scikit-learn and TensorFlow are used and applied to data analysis and demand forecasting.

[0109] First, the user enters their ride request using a terminal. They enter their current location, destination, and desired conditions (such as a quiet vehicle or a specific mode of transport) through the terminal's user interface. The entered information is processed by an application installed on the terminal and sent to the server.

[0110] The server analyzes the received information and selects the optimal mode of transport. In this process, the server learns the user's individual transport preferences based on their past usage history. It also analyzes real-time traffic conditions and local demand forecasts to determine the optimal vehicle allocation. The server aggregates this information and transmits it to the terminal via communication, along with optimized route information.

[0111] The terminal displays the received information to the user, visually showing the transportation details. This allows the user to easily check the recommended mode of transport and its details. Once the confirmation process is complete, the selected mode of transport is arranged.

[0112] For example, suppose a user is planning a trip to a new shopping mall and desires a highly-rated driver and a quiet ride. In this case, the server considers the user's past travel history and current traffic conditions to instantly select a suitable autonomous vehicle and suggest it to the user's device. The user can then check the vehicle's estimated arrival time in real time on their device.

[0113] Example prompt message: "Please specify your destination. Do you prefer a quiet ride? The best route and vehicle will be suggested considering current traffic conditions."

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

[0115] Step 1:

[0116] The user enters their ride request using a terminal. The information entered includes their current location, destination, and preferred conditions (e.g., quiet interior). This information is collected by the terminal's interface and formatted as digital data. This data is then converted to JSON format and prepared for transmission to the server.

[0117] Step 2:

[0118] The terminal sends the data entered by the user to the server. This data is sent to the server via a stable network using a communication method. The transmitted data undergoes initial data analysis processing on the server.

[0119] Step 3:

[0120] The server performs data analysis based on the received user data. Specifically, geographical location, destination, and the user's past usage history are retrieved from the database, and this information is used to select the optimal mode of transport. This analysis is performed using Scikit-learn or TensorFlow. The output is a list of optimized modes of transport.

[0121] Step 4:

[0122] The server uses demand forecasting methods to analyze traffic conditions in real time. This involves using historical travel patterns and current traffic data. The server integrates these results to determine the optimal deployment of transportation methods. As a result, optimal route information is generated.

[0123] Step 5:

[0124] The server transmits the selected means of transport and route information to the terminal. This information is transmitted quickly using communication methods.

[0125] Step 6:

[0126] The terminal visually displays information received from the server to the user. The user interface clearly shows the user operational information and recommended modes of transport. This allows the user to confirm their boarding based on the information presented.

[0127] Step 7:

[0128] After the user reviews the presented information, they perform a "confirmation" operation through their device. Based on the user's consent, the device notifies the server. The server receives this notification and makes the final arrangements for the selected means of transport.

[0129] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0130] This invention is a system that combines the optimization of conventional means of transportation with an emotion engine that recognizes the user's emotional state. This system consists of three main components: a server, a terminal, and a user.

[0131] First, the user enters their ride request using a terminal application. This typically includes their geographical location and destination, as well as their preferences (vehicle type, quiet environment, etc.). The terminal is also equipped with a camera and microphone to capture the user's facial expressions and voice. This data is analyzed by an emotion engine to recognize the user's emotional state (e.g., stress, anxiety, joy).

[0132] The recognized emotion data, along with other request information, is sent to the server in real time. The server uses data analysis tools to comprehensively analyze the user's request, past usage history, and emotional state. The emotional state analyzed by the emotion engine is reflected in the transportation selection process. For example, if the user is feeling stressed, the system prioritizes selecting a transportation method that provides a more comfortable in-vehicle environment.

[0133] Furthermore, the server uses demand forecasting tools to predict local demand and, while also considering the emotional state of users, determines the optimal placement of transportation methods. The selected transportation methods and optimal route information, transmitted through the instruction tools, are sent to the user's terminal and also notified to the transportation methods.

[0134] The terminal presents the user with information on transportation options, fares, and emotionally-based service adjustments received from the server. Once the user confirms this, the arrangement of the selected transportation method is completed. Through this process, users can enjoy a travel experience tailored to their emotional state.

[0135] For example, if a user is feeling stressed on their way to an important presentation, the emotion engine detects this state, and the server prioritizes selecting a vehicle with a quiet environment and a highly-rated driver. This allows the user to reach their destination in a calm state. In this way, the present invention realizes the provision of new value that takes the user's emotions into consideration.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] The user opens a terminal application and enters their current location, destination, and preferred ride conditions. Emotional data is collected from the user's facial expressions and voice using the camera and microphone.

[0139] Step 2:

[0140] The device sends the information entered by the user and the collected sentiment data to the server.

[0141] Step 3:

[0142] The server processes the user request information it receives using data analysis tools and retrieves past usage history and preference data from a database.

[0143] Step 4:

[0144] The server's emotion engine analyzes the user's facial expressions and voice data to identify the user's emotional state. For example, it might analyze that the user is feeling stressed.

[0145] Step 5:

[0146] Based on the analysis results, the server selects a mode of transportation that matches the user's emotional state and preferences. For example, it might prioritize vehicles with a quiet environment to reduce stress.

[0147] Step 6:

[0148] The server analyzes local demand using demand forecasting tools and plans the optimal placement of transportation methods. It also takes emotional states into consideration to enable more comfortable choices.

[0149] Step 7:

[0150] The server sends the selected transportation method, calculated optimal route, fare information, and emotion-based service recommendations to the user's terminal.

[0151] Step 8:

[0152] The terminal displays all information received from the server to the user and requests final confirmation.

[0153] Step 9:

[0154] The user is satisfied with the information presented and taps the confirmation button. The confirmation is sent to the server.

[0155] Step 10:

[0156] Once the server receives user confirmation, it uses the instruction mechanism to send action instructions to the selected means of transport. The means of transport is notified of the optimal route and estimated arrival time.

[0157] Step 11:

[0158] Once the user's ride is complete, the data obtained by the emotion engine is saved to a database for further optimization in future rides.

[0159] (Example 2)

[0160] 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".

[0161] When users utilize transportation, they need to select the optimal mode of transport not only based on geographical factors and mere preferences, but also taking into account their emotional state at the time. However, conventional systems have failed to adequately reflect users' emotional states, sometimes leading to decreased satisfaction. Therefore, improving the quality of the user's travel experience and reducing stress is a challenge.

[0162] 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.

[0163] In this invention, the server includes information processing means, learning means, prediction means, analysis means, and control means. This makes it possible to comprehensively analyze the user's geographical information, preferences, and emotional state and select the optimal means of transportation.

[0164] "Information processing means" refers to the processing performed within a system to select the optimal means of transportation based on the user's geographical location information and destination information.

[0165] A "learning method" is a function that establishes a process for determining the most suitable mode of transportation for a user, based on data regarding the user's preferences and emotional state.

[0166] A "predictive method" is a technique used to predict demand and determine the optimal allocation of transportation methods based on past usage trends of transportation methods.

[0167] "Analysis means" refers to a process that analyzes the user's voice and facial expression data and has the function of evaluating their emotional state.

[0168] "Control means" refers to a function within the system that provides instructions to the selected means of transport and ensures the smooth execution of the actual transport process.

[0169] The present invention provides a means of transportation that takes into account the user's geographical information, preferences, and emotional state in order to optimize the user's movement. This system mainly consists of three main components: a server, a terminal, and a user.

[0170] Users make travel requests using a terminal application. This application is downloaded to smartphones and tablets and accepts user input. The information collected includes the current location, destination, and transportation preferences (e.g., luxury car, quiet environment). In addition, the terminal uses a camera and microphone to capture the user's facial expressions and voice data.

[0171] The data collected by the device is transmitted in real time to a server within the system. On the server, an emotion analysis algorithm processes this data and evaluates the user's emotional state (e.g., stress, anxiety, joy). In this process, existing machine learning models are utilized for emotion analysis.

[0172] The server uses information processing tools to comprehensively analyze user request information, sentiment analysis results, and past travel history data. This allows for the selection of the most suitable mode of transportation for each user. Furthermore, demand forecasting models are used to predict regional demand, enabling the optimal allocation of transportation. The selected mode of transportation and route information are transmitted from the server to the terminal and presented. Finally, the user confirms this information, and the selected arrangements are completed.

[0173] For example, if a user is experiencing high levels of stress during their commute, the emotion engine immediately recognizes this state and selects a mode of transportation that provides a quieter and more relaxing environment. In this way, the system provides a comfortable travel service based on the user's emotions.

[0174] An example of a prompt message generated using an AI model is, "If the user is perceived as feeling stressed, please suggest the most suitable mode of transportation."

[0175] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0176] Step 1:

[0177] The user launches the terminal application and enters a ride request. The input data includes the current location (GPS data), destination, and preferred conditions for the ride (e.g., quiet environment, specific vehicle type). This information is organized and prepared in the appropriate format by the terminal. The output is a ride request information packet.

[0178] Step 2:

[0179] The device uses a camera and microphone to capture the user's facial expressions and voice. This data is processed in real time and generated as input data to determine the user's emotional state. The output consists of audio and video data for emotion analysis.

[0180] Step 3:

[0181] The server receives boarding request information packets and audio / video data for emotion analysis transmitted from the terminal. The server executes an emotion analysis algorithm to evaluate the user's emotional state. The output obtained from this process is evaluation information indicating the user's emotional state.

[0182] Step 4:

[0183] The server uses information processing tools to integrate ride request information, emotional state evaluation information, and past usage history data to select the optimal mode of transportation. A pattern recognition algorithm is used for comprehensive analysis. The output is a customized proposal for transportation for each user.

[0184] Step 5:

[0185] The server uses demand forecasting tools to predict regional demand. The server runs a forecasting model based on historical movement data to determine the optimal placement. The output of this process is information on the placement of transportation methods based on the forecast.

[0186] Step 6:

[0187] The terminal displays suggested transportation options received from the server to the user. This information includes details of the transportation method, fares, and route options. An interface is provided for the user to review and make a selection. The output is the transportation information presented to the user.

[0188] Step 7:

[0189] The user makes a selection based on the provided information. They then perform an action to notify the server that arrangements for the selected mode of transport are complete. This completes the system-wide operation. The output is information about the final decided mode of transport.

[0190] (Application Example 2)

[0191] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0192] Traditional transportation selection is based solely on the user's geographical location and destination, making it difficult to provide services tailored to the individual user's emotional state. This has resulted in situations where, even when users are stressed or experiencing specific emotional states, the appropriate travel experience cannot be provided. Therefore, there is a need to recognize each user's emotional state and select the most suitable mode of transportation accordingly, thereby providing a more personalized travel experience.

[0193] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0194] In this invention, the server includes information analysis means, learning means, demand forecasting means, and sentiment analysis means. This makes it possible to optimize the means of transportation by taking into account the user's geographical location and destination, as well as their emotional state at that time.

[0195] "Information analysis means" refers to a function that analyzes data such as the user's geographical location, destination, and past usage history in order to select the most suitable means of transportation.

[0196] A "learning tool" is a function that takes into account the user's preferences and past choices to inform the selection of the means of transportation.

[0197] A "demand forecasting tool" is a function that predicts the demand for transportation based on past usage patterns of modes of transport.

[0198] A "controlling mechanism" is a function that instructs the user on how the selected means of transport should operate.

[0199] "Emotional analysis tools" are functions that recognize the user's emotional state and adjust the selection of transportation methods based on that.

[0200] In the embodiments of this invention, the system is implemented using three main components: a user, a terminal, and a server.

[0201] ■System Configuration

[0202] Users utilize smartphones or smart glasses equipped with cameras and microphones. These devices capture the user's facial expressions and voice, and transmit them to an emotion analysis system. This system uses software called an emotion engine to analyze the user's emotional state.

[0203] ■Data Processing

[0204] The server processes data as an information analysis tool. It comprehensively analyzes the user's geographical location, destination, past usage history, and emotional state to select the optimal mode of transport and route. In this process, a generative AI model is used to enhance route suggestions based on the user's emotional state. The selected mode of transport and route information is transmitted to the user's terminal via an instruction device.

[0205] For example, if a user is in a highly stressed state, the emotion analysis system will detect this state, and the server will prioritize selecting a quiet and calming mode of transport. Relaxing music will be played during transport.

[0206] By providing the system with a prompt such as, "Please suggest the optimal travel route for this user to relax. The user is feeling stressed," the emotion analysis and information analysis tools can work together to provide the most suitable service.

[0207] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0208] Step 1:

[0209] The user enters their ride request into a terminal application using a smartphone or smart glasses. During this process, the terminal captures information such as geographical location, destination, and preferred vehicle type, as well as the user's facial expressions and voice. The terminal then transmits the captured data to a sentiment analysis system.

[0210] Step 2:

[0211] The device uses an emotion engine to analyze the user's emotional state using captured facial expressions and voice data as input. Specifically, it recognizes the user's emotional state, such as stress, anxiety, and joy, from this data and outputs the result. The emotional state data is sent to a server.

[0212] Step 3:

[0213] The server receives geographical location, destination, user's past usage history, and emotional state as input for information analysis. This data is integrated, and a generative AI model is used to calculate and output the optimal mode of transport and route. During this process, the user's emotional state is taken into consideration when selecting the mode of transport.

[0214] Step 4:

[0215] The server uses demand forecasting tools to predict the demand for transportation methods across the entire region. Using past travel patterns as input, it determines and outputs the optimal allocation of transportation methods that are appropriate to the current situation.

[0216] Step 5:

[0217] The server transmits the selected mode of transport and route information to the user's terminal via a command system. During transport, suggestions are made to provide the user with a more comfortable travel experience (e.g., playing relaxing music).

[0218] Step 6:

[0219] Users review information about the received transportation method and experience the provided service. If necessary, they can submit feedback via their device, and data is collected to help improve future use.

[0220] Through the above processing flow, users can enjoy an optimal travel experience tailored to their emotional state.

[0221] 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.

[0222] Data generation model 58 is a type of 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.

[0223] 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 be performed by the smart device 14.

[0224] [Second Embodiment]

[0225] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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).

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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".

[0237] This invention provides a system for optimizing means of transportation. The system consists of three main components: a server, a terminal, and a user.

[0238] First, the user enters a ride request using a terminal application. The user can specify their current location and destination, and enter their preferred conditions (for example, a quiet interior or a specific vehicle type). The user's input information is sent to the server in real time.

[0239] The server has data analysis tools designed to analyze geographical location and destination data provided by users, as well as past usage history and preferences. A learning tool on the server records each user's past travel history and learns the patterns. Based on this information, it selects a mode of transportation that matches the user's preferences.

[0240] Furthermore, the server uses demand forecasting tools to analyze current traffic conditions and area demand in real time. Based on this analysis, it determines the optimal allocation of transportation. For example, if demand is high in a particular area, transportation will be concentrated in that area.

[0241] The selected mode of transport and optimized route information are transmitted from the server to the user's terminal. The server also provides detailed operational instructions (such as estimated arrival time and recommended route) to the selected mode of transport using an instruction mechanism.

[0242] The terminal displays information sent from the server to the user and requests confirmation. If the user is satisfied with the presented transportation method and fare information, selecting "Confirm" completes the arrangement of transportation. Through this procedure, users can travel to their destination efficiently without waiting time.

[0243] As a concrete example, suppose a user wants to travel to a meeting within the city and prefers a highly-rated driver and a comfortable ride. In this case, the server analyzes past patterns and current traffic conditions to instantly select and provide a mode of transportation that matches the user's preferences. This process ensures that the user enjoys a satisfying travel experience.

[0244] The following describes the processing flow.

[0245] Step 1:

[0246] The user opens the terminal application and enters their current location, destination, and ride preferences. This information is then sent from the terminal to the server.

[0247] Step 2:

[0248] The server receives information from the user and uses data analysis tools to retrieve the user's past usage history and preferences from the database.

[0249] Step 3:

[0250] The server uses learning mechanisms to select a transportation method that best suits the user's preferences based on user input and past history.

[0251] Step 4:

[0252] The server uses demand forecasting tools to analyze the demand across the entire region in real time and optimize the allocation of selected transportation methods.

[0253] Step 5:

[0254] The server sends the selected transportation method, optimal route, and pre-calculated fare to the user's terminal.

[0255] Step 6:

[0256] The terminal displays information received from the server to the user, and the user confirms the presented information.

[0257] Step 7:

[0258] If the user is satisfied with the proposed transportation method and price, they tap the "Confirm" button. This confirmation is then sent from the device to the server.

[0259] Step 8:

[0260] The server receives user confirmation and sends action instructions (estimated arrival time and optimal route) to the selected means of transport using the instruction mechanism.

[0261] (Example 1)

[0262] 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."

[0263] To achieve efficient use of transportation and improve user satisfaction, accurate selection of transportation methods based on user requests and optimal allocation according to demand are necessary. However, previous systems have struggled to analyze user requests in real time and provide efficient transportation methods. In particular, there has been a problem in that the rapid selection of transportation methods, taking into account traffic conditions and user preferences, has not been adequately performed.

[0264] 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.

[0265] In this invention, the server includes information analysis means, a learning device, and a request prediction device. This enables the selection of the optimal means of transport based on the user's geographical location, destination, and preferences, and the optimal allocation of means of transport according to demand.

[0266] "Information analysis means" refers to technology that analyzes the user's geographical location and destination information to select the appropriate means of transportation.

[0267] A "learning device" is a machine that learns patterns based on the user's past usage history and preferences, and uses this information to select the appropriate means of transportation.

[0268] A "demand forecasting device" is a mechanism that forecasts demand in each region and optimally allocates transportation methods.

[0269] A "communication device" is a device that provides selected means of transport and optimized route information based on information received from the user's terminal.

[0270] An "instruction device" is a machine that provides detailed operational instructions to a selected means of transport.

[0271] This system aims to optimize transportation by effectively managing transportation methods using information analysis tools, learning devices, and request prediction devices. It primarily revolves around three components: a server, terminals, and users, all working together to function.

[0272] The server collects and analyzes geographic location and destination data sent from each user as a means of information analysis. Databases and data analysis software are used for this process. The technologies employed include, for example, Python libraries and analysis algorithms using the R language. Furthermore, a generative AI model is employed to learn from past travel history, utilizing scikit-learn and TensorFlow to thoroughly learn user patterns.

[0273] The terminal provides an interface for user interaction. When a user enters a ride request, the terminal quickly transmits that information to the server. The information transmitted from the terminal is processed by the server in real time, and the results are fed back to the user. This communication utilizes common mobile communication technologies and internet protocols.

[0274] Through a terminal application, users specify their current location, destination, and special requirements (e.g., "quiet interior" or "highly-rated driver"). This allows the system to select the most suitable mode of transportation based on the user's needs. A concrete example is when a user plans to travel to a meeting within the city; the system suggests appropriate taxis or rideshare services.

[0275] For demand forecasting, the server performs real-time traffic analysis and area-specific demand analysis. In some cases, an automated geographic information system (GIS) is used. Based on the obtained data, the server runs a model to most efficiently allocate transportation methods, enabling a rapid response.

[0276] A concrete example of a prompt is the input, "Please suggest the best mode of transportation based on the user's current location, destination, and preferences." This allows the generative AI model to perform appropriate data analysis and provide the most satisfying mode of transportation for the user.

[0277] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0278] Step 1:

[0279] The user enters their ride request using a terminal application. The user enters their current location, destination, and desired conditions (e.g., quiet interior, specific vehicle type). This information is entered through the terminal's UI and prepared as data in JSON format. The terminal then prepares to send this data to the server. The entered information embodies the user's travel request.

[0280] Step 2:

[0281] The terminal transmits JSON-formatted data obtained from the user to the server in real time. This transmission utilizes mobile networks and internet communication, and is conducted via the HTTP protocol. This ensures that user requests are quickly and reliably transmitted to the server. The terminal's output is user request information encoded in a format that the server can parse.

[0282] Step 3:

[0283] The server analyzes the received data. For analysis, the server uses a generative AI model and analyzes user data, past usage history, and current traffic information. Specifically, it uses data analysis tools such as Python and R. The input data is clustered and categorized to determine which mode of transport is optimal. This process generates an output that selects the most suitable mode of transport for the user.

[0284] Step 4:

[0285] The server performs demand forecasting. Geographical information and traffic data collected in real time are used for demand forecasting. The server utilizes a GIS tool to predict demand in a specific area and calculates the optimal allocation of transportation means based on the prediction. The input is the current geographical information data and traffic data, and the output is the guidelines for the allocation of transportation means.

[0286] Step 5:

[0287] The server transmits real-time information including the optimal transportation means and route information to the terminal. This information includes the selected transportation means, the recommended route, and the estimated arrival time. The HTTP protocol is used again for this communication, and real-time performance is required. As the output of the server, a process of providing the selected information to the user is carried out.

[0288] Step 6:

[0289] The terminal presents the information received from the server to the user. The user checks this, and if they agree to the proposed transportation means and fare, they operate the "Confirm" button to accept. The input is the transportation means information from the server, and the output is the data that is resent to the server as the user's agreement or modification request.

[0290] Step 7:

[0291] The server sends an instruction to the transportation means upon receiving the user's confirmation. This includes the operation schedule, the recommended route, and special requests. Details are sent to the corresponding transportation service via the communication device of the server. As a result of the instruction, the selected transportation means can quickly head towards the user.

[0292] (Application Example 1)

[0293] 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."

[0294] In modern society, there is a demand for efficient and customized means of transportation. However, challenges remain in selecting the optimal mode of transport to suit the diverse preferences of each user and providing real-time monitoring of transportation information. Therefore, improving convenience during travel and enhancing the user experience are crucial.

[0295] 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.

[0296] In this invention, the server includes data analysis means for selecting the optimal mode of transport based on the user's geographical location and destination; learning means for learning the user's preferences and reflecting them in the selection of the mode of transport; and communication means for transmitting real-time operational information of the mode of transport to the user terminal and requesting confirmation. As a result, the user can select the optimal mode of transport according to their preferences in real time and visually monitor the operational status on their terminal.

[0297] "Means of transportation" refers to all means of transporting users from one point to another.

[0298] "User" refers to an individual or group that wishes to travel using a means of transportation.

[0299] "Geographic location" refers to specific geographical coordinates obtained using the device's location information function.

[0300] "Destination" refers to the point designated by the user as the final stop of their journey.

[0301] "Transportation means" refers to the specific means of transport or devices used to achieve movement.

[0302] The "data analysis means" refers to a device or software that analyzes the geographical location of the user and the information of the destination and has the function of selecting the optimal means of transportation.

[0303] The "learning means" refers to a device or software that accumulates data on the past behaviors and preferences of the user and has the function of selecting the most suitable means of transportation for the user based on this data.

[0304] The "demand prediction means" refers to a device or software that analyzes the past usage patterns of transportation means and the current situation and has the function of predicting the future demand for transportation means.

[0305] The "instruction means" refers to a device or software that has the function of providing detailed operation instructions and recommended routes for the selected means of transportation.

[0306] The "communication means" refers to a device or software that has the function of enabling the server and the user's terminal to send and receive data bidirectionally.

[0307] The "display means" refers to a device or software that has the function of visually presenting information to the user on the terminal.

[0308] The system for implementing this invention consists of three main elements: a server, a terminal, and a user. The server is constructed using programming languages such as Python and Java and is operated on hardware with the ability to process a large amount of data at high speed. Machine learning libraries such as Scikit-learn and TensorFlow are used and applied to data analysis and demand prediction.

[0309] First, the user inputs a ride request using the terminal. The current location, destination, and desired conditions (such as a quiet vehicle interior or a specific transportation vehicle, etc.) are input from the user interface of the terminal. The input information is processed by the application installed on the terminal and sent to the server.

[0310] The server analyzes the received information and selects the optimal mode of transport. In this process, the server learns the user's individual transport preferences based on their past usage history. It also analyzes real-time traffic conditions and local demand forecasts to determine the optimal vehicle allocation. The server aggregates this information and transmits it to the terminal via communication, along with optimized route information.

[0311] The terminal displays the received information to the user, visually showing the transportation details. This allows the user to easily check the recommended mode of transport and its details. Once the confirmation process is complete, the selected mode of transport is arranged.

[0312] For example, suppose a user is planning a trip to a new shopping mall and desires a highly-rated driver and a quiet ride. In this case, the server considers the user's past travel history and current traffic conditions to instantly select a suitable autonomous vehicle and suggest it to the user's device. The user can then check the vehicle's estimated arrival time in real time on their device.

[0313] Example prompt message: "Please specify your destination. Do you prefer a quiet ride? The best route and vehicle will be suggested considering current traffic conditions."

[0314] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0315] Step 1:

[0316] The user enters their ride request using a terminal. The information entered includes their current location, destination, and preferred conditions (e.g., quiet interior). This information is collected by the terminal's interface and formatted as digital data. This data is then converted to JSON format and prepared for transmission to the server.

[0317] Step 2:

[0318] The terminal sends the data entered by the user to the server. This data is sent to the server via a stable network using a communication method. The transmitted data undergoes initial data analysis processing on the server.

[0319] Step 3:

[0320] The server performs data analysis based on the received user data. Specifically, geographical location, destination, and the user's past usage history are retrieved from the database, and this information is used to select the optimal mode of transport. This analysis is performed using Scikit-learn or TensorFlow. The output is a list of optimized modes of transport.

[0321] Step 4:

[0322] The server uses demand forecasting methods to analyze traffic conditions in real time. This involves using historical travel patterns and current traffic data. The server integrates these results to determine the optimal deployment of transportation methods. As a result, optimal route information is generated.

[0323] Step 5:

[0324] The server transmits the selected means of transport and route information to the terminal. This information is transmitted quickly using communication methods.

[0325] Step 6:

[0326] The terminal visually displays information received from the server to the user. The user interface clearly shows the user operational information and recommended modes of transport. This allows the user to confirm their boarding based on the information presented.

[0327] Step 7:

[0328] After the user reviews the presented information, they perform a "confirmation" operation through their device. Based on the user's consent, the device notifies the server. The server receives this notification and makes the final arrangements for the selected means of transport.

[0329] 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.

[0330] This invention is a system that combines the optimization of conventional means of transportation with an emotion engine that recognizes the user's emotional state. This system consists of three main components: a server, a terminal, and a user.

[0331] First, the user enters their ride request using a terminal application. This typically includes their geographical location and destination, as well as their preferences (vehicle type, quiet environment, etc.). The terminal is also equipped with a camera and microphone to capture the user's facial expressions and voice. This data is analyzed by an emotion engine to recognize the user's emotional state (e.g., stress, anxiety, joy).

[0332] The recognized emotion data, along with other request information, is sent to the server in real time. The server uses data analysis tools to comprehensively analyze the user's request, past usage history, and emotional state. The emotional state analyzed by the emotion engine is reflected in the transportation selection process. For example, if the user is feeling stressed, the system prioritizes selecting a transportation method that provides a more comfortable in-vehicle environment.

[0333] Furthermore, the server uses demand forecasting tools to predict local demand and, while also considering the emotional state of users, determines the optimal placement of transportation methods. The selected transportation methods and optimal route information, transmitted through the instruction tools, are sent to the user's terminal and also notified to the transportation methods.

[0334] The terminal presents the user with information on transportation options, fares, and emotionally-based service adjustments received from the server. Once the user confirms this, the arrangement of the selected transportation method is completed. Through this process, users can enjoy a travel experience tailored to their emotional state.

[0335] For example, if a user is feeling stressed on their way to an important presentation, the emotion engine detects this state, and the server prioritizes selecting a vehicle with a quiet environment and a highly-rated driver. This allows the user to reach their destination in a calm state. In this way, the present invention realizes the provision of new value that takes the user's emotions into consideration.

[0336] The following describes the processing flow.

[0337] Step 1:

[0338] The user opens a terminal application and enters their current location, destination, and preferred ride conditions. Emotional data is collected from the user's facial expressions and voice using the camera and microphone.

[0339] Step 2:

[0340] The device sends the information entered by the user and the collected sentiment data to the server.

[0341] Step 3:

[0342] The server processes the user request information it receives using data analysis tools and retrieves past usage history and preference data from a database.

[0343] Step 4:

[0344] The server's emotion engine analyzes the user's facial expressions and voice data to identify the user's emotional state. For example, it might analyze that the user is feeling stressed.

[0345] Step 5:

[0346] Based on the analysis results, the server selects a mode of transportation that matches the user's emotional state and preferences. For example, it might prioritize vehicles with a quiet environment to reduce stress.

[0347] Step 6:

[0348] The server analyzes local demand using demand forecasting tools and plans the optimal placement of transportation methods. It also takes emotional states into consideration to enable more comfortable choices.

[0349] Step 7:

[0350] The server sends the selected transportation method, calculated optimal route, fare information, and emotion-based service recommendations to the user's terminal.

[0351] Step 8:

[0352] The terminal displays all information received from the server to the user and requests final confirmation.

[0353] Step 9:

[0354] The user is satisfied with the information presented and taps the confirmation button. The confirmation is sent to the server.

[0355] Step 10:

[0356] Once the server receives user confirmation, it uses the instruction mechanism to send action instructions to the selected means of transport. The means of transport is notified of the optimal route and estimated arrival time.

[0357] Step 11:

[0358] Once the user's ride is complete, the data obtained by the emotion engine is saved to a database for further optimization in future rides.

[0359] (Example 2)

[0360] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0361] When users utilize transportation, they need to select the optimal mode of transport not only based on geographical factors and mere preferences, but also taking into account their emotional state at the time. However, conventional systems have failed to adequately reflect users' emotional states, sometimes leading to decreased satisfaction. Therefore, improving the quality of the user's travel experience and reducing stress is a challenge.

[0362] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0363] In this invention, the server includes information processing means, learning means, prediction means, analysis means, and control means. This makes it possible to comprehensively analyze the user's geographical information, preferences, and emotional state and select the optimal means of transportation.

[0364] "Information processing means" refers to the processing performed within a system to select the optimal means of transportation based on the user's geographical location information and destination information.

[0365] A "learning method" is a function that establishes a process for determining the most suitable mode of transportation for a user, based on data regarding the user's preferences and emotional state.

[0366] A "predictive method" is a technique used to predict demand and determine the optimal allocation of transportation methods based on past usage trends of transportation methods.

[0367] "Analysis means" refers to a process that analyzes the user's voice and facial expression data and has the function of evaluating their emotional state.

[0368] "Control means" refers to a function within the system that provides instructions to the selected means of transport and ensures the smooth execution of the actual transport process.

[0369] The present invention provides a means of transportation that takes into account the user's geographical information, preferences, and emotional state in order to optimize the user's movement. This system mainly consists of three main components: a server, a terminal, and a user.

[0370] Users make travel requests using a terminal application. This application is downloaded to smartphones and tablets and accepts user input. The information collected includes the current location, destination, and transportation preferences (e.g., luxury car, quiet environment). In addition, the terminal uses a camera and microphone to capture the user's facial expressions and voice data.

[0371] The data collected by the device is transmitted in real time to a server within the system. On the server, an emotion analysis algorithm processes this data and evaluates the user's emotional state (e.g., stress, anxiety, joy). In this process, existing machine learning models are utilized for emotion analysis.

[0372] The server uses information processing tools to comprehensively analyze user request information, sentiment analysis results, and past travel history data. This allows for the selection of the most suitable mode of transportation for each user. Furthermore, demand forecasting models are used to predict regional demand, enabling the optimal allocation of transportation. The selected mode of transportation and route information are transmitted from the server to the terminal and presented. Finally, the user confirms this information, and the selected arrangements are completed.

[0373] For example, if a user is experiencing high levels of stress during their commute, the emotion engine immediately recognizes this state and selects a mode of transportation that provides a quieter and more relaxing environment. In this way, the system provides a comfortable travel service based on the user's emotions.

[0374] An example of a prompt message generated using an AI model is, "If the user is perceived as feeling stressed, please suggest the most suitable mode of transportation."

[0375] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0376] Step 1:

[0377] The user launches the terminal application and enters a ride request. The input data includes the current location (GPS data), destination, and preferred conditions for the ride (e.g., quiet environment, specific vehicle type). This information is organized and prepared in the appropriate format by the terminal. The output is a ride request information packet.

[0378] Step 2:

[0379] The device uses a camera and microphone to capture the user's facial expressions and voice. This data is processed in real time and generated as input data to determine the user's emotional state. The output consists of audio and video data for emotion analysis.

[0380] Step 3:

[0381] The server receives boarding request information packets and audio / video data for emotion analysis transmitted from the terminal. The server executes an emotion analysis algorithm to evaluate the user's emotional state. The output obtained from this process is evaluation information indicating the user's emotional state.

[0382] Step 4:

[0383] The server uses information processing tools to integrate ride request information, emotional state evaluation information, and past usage history data to select the optimal mode of transportation. A pattern recognition algorithm is used for comprehensive analysis. The output is a customized proposal for transportation for each user.

[0384] Step 5:

[0385] The server uses demand forecasting tools to predict regional demand. The server runs a forecasting model based on historical movement data to determine the optimal placement. The output of this process is information on the placement of transportation methods based on the forecast.

[0386] Step 6:

[0387] The terminal displays suggested transportation options received from the server to the user. This information includes details of the transportation method, fares, and route options. An interface is provided for the user to review and make a selection. The output is the transportation information presented to the user.

[0388] Step 7:

[0389] The user makes a selection based on the provided information. They then perform an action to notify the server that arrangements for the selected mode of transport are complete. This completes the system-wide operation. The output is information about the final decided mode of transport.

[0390] (Application Example 2)

[0391] 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."

[0392] Traditional transportation selection is based solely on the user's geographical location and destination, making it difficult to provide services tailored to the individual user's emotional state. This has resulted in situations where, even when users are stressed or experiencing specific emotional states, the appropriate travel experience cannot be provided. Therefore, there is a need to recognize each user's emotional state and select the most suitable mode of transportation accordingly, thereby providing a more personalized travel experience.

[0393] 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.

[0394] In this invention, the server includes information analysis means, learning means, demand forecasting means, and sentiment analysis means. This makes it possible to optimize the means of transportation by taking into account the user's geographical location and destination, as well as their emotional state at that time.

[0395] "Information analysis means" refers to a function that analyzes data such as the user's geographical location, destination, and past usage history in order to select the most suitable means of transportation.

[0396] A "learning tool" is a function that takes into account the user's preferences and past choices to inform the selection of the means of transportation.

[0397] A "demand forecasting tool" is a function that predicts the demand for transportation based on past usage patterns of modes of transport.

[0398] A "controlling mechanism" is a function that instructs the user on how the selected means of transport should operate.

[0399] "Emotional analysis tools" are functions that recognize the user's emotional state and adjust the selection of transportation methods based on that.

[0400] In the embodiments of this invention, the system is implemented using three main components: a user, a terminal, and a server.

[0401] ■System Configuration

[0402] Users utilize smartphones or smart glasses equipped with cameras and microphones. These devices capture the user's facial expressions and voice, and transmit them to an emotion analysis system. This system uses software called an emotion engine to analyze the user's emotional state.

[0403] ■Data Processing

[0404] The server processes data as an information analysis tool. It comprehensively analyzes the user's geographical location, destination, past usage history, and emotional state to select the optimal mode of transport and route. In this process, a generative AI model is used to enhance route suggestions based on the user's emotional state. The selected mode of transport and route information is transmitted to the user's terminal via an instruction device.

[0405] For example, if a user is in a highly stressed state, the emotion analysis system will detect this state, and the server will prioritize selecting a quiet and calming mode of transport. Relaxing music will be played during transport.

[0406] By providing the system with a prompt such as, "Please suggest the optimal travel route for this user to relax. The user is feeling stressed," the emotion analysis and information analysis tools can work together to provide the most suitable service.

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

[0408] Step 1:

[0409] The user enters their ride request into a terminal application using a smartphone or smart glasses. During this process, the terminal captures information such as geographical location, destination, and preferred vehicle type, as well as the user's facial expressions and voice. The terminal then transmits the captured data to a sentiment analysis system.

[0410] Step 2:

[0411] The device uses an emotion engine to analyze the user's emotional state using captured facial expressions and voice data as input. Specifically, it recognizes the user's emotional state, such as stress, anxiety, and joy, from this data and outputs the result. The emotional state data is sent to a server.

[0412] Step 3:

[0413] The server receives geographical location, destination, user's past usage history, and emotional state as input for information analysis. This data is integrated, and a generative AI model is used to calculate and output the optimal mode of transport and route. During this process, the user's emotional state is taken into consideration when selecting the mode of transport.

[0414] Step 4:

[0415] The server uses demand forecasting tools to predict the demand for transportation methods across the entire region. Using past travel patterns as input, it determines and outputs the optimal allocation of transportation methods that are appropriate to the current situation.

[0416] Step 5:

[0417] The server transmits the selected mode of transport and route information to the user's terminal via a command system. During transport, suggestions are made to provide the user with a more comfortable travel experience (e.g., playing relaxing music).

[0418] Step 6:

[0419] Users review information about the received transportation method and experience the provided service. If necessary, they can submit feedback via their device, and data is collected to help improve future use.

[0420] Through the above processing flow, users can enjoy an optimal travel experience tailored to their emotional state.

[0421] 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.

[0422] 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.

[0423] 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.

[0424] [Third Embodiment]

[0425] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0426] 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.

[0427] 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).

[0428] 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.

[0429] 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.

[0430] 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).

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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.

[0435] 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.

[0436] 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".

[0437] This invention provides a system for optimizing means of transportation. The system consists of three main components: a server, a terminal, and a user.

[0438] First, the user enters a ride request using a terminal application. The user can specify their current location and destination, and enter their preferred conditions (for example, a quiet interior or a specific vehicle type). The user's input information is sent to the server in real time.

[0439] The server has data analysis tools designed to analyze geographical location and destination data provided by users, as well as past usage history and preferences. A learning tool on the server records each user's past travel history and learns the patterns. Based on this information, it selects a mode of transportation that matches the user's preferences.

[0440] Furthermore, the server uses demand forecasting tools to analyze current traffic conditions and area demand in real time. Based on this analysis, it determines the optimal allocation of transportation. For example, if demand is high in a particular area, transportation will be concentrated in that area.

[0441] The selected mode of transport and optimized route information are transmitted from the server to the user's terminal. The server also provides detailed operational instructions (such as estimated arrival time and recommended route) to the selected mode of transport using an instruction mechanism.

[0442] The terminal displays information sent from the server to the user and requests confirmation. If the user is satisfied with the presented transportation method and fare information, selecting "Confirm" completes the arrangement of transportation. Through this procedure, users can travel to their destination efficiently without waiting time.

[0443] As a concrete example, suppose a user wants to travel to a meeting within the city and prefers a highly-rated driver and a comfortable ride. In this case, the server analyzes past patterns and current traffic conditions to instantly select and provide a mode of transportation that matches the user's preferences. This process ensures that the user enjoys a satisfying travel experience.

[0444] The following describes the processing flow.

[0445] Step 1:

[0446] The user opens the terminal application and enters their current location, destination, and ride preferences. This information is then sent from the terminal to the server.

[0447] Step 2:

[0448] The server receives information from the user and uses data analysis tools to retrieve the user's past usage history and preferences from the database.

[0449] Step 3:

[0450] The server uses learning mechanisms to select a transportation method that best suits the user's preferences based on user input and past history.

[0451] Step 4:

[0452] The server uses demand forecasting tools to analyze the demand across the entire region in real time and optimize the allocation of selected transportation methods.

[0453] Step 5:

[0454] The server sends the selected transportation method, optimal route, and pre-calculated fare to the user's terminal.

[0455] Step 6:

[0456] The terminal displays information received from the server to the user, and the user confirms the presented information.

[0457] Step 7:

[0458] If the user is satisfied with the proposed transportation method and price, they tap the "Confirm" button. This confirmation is then sent from the device to the server.

[0459] Step 8:

[0460] The server receives user confirmation and sends action instructions (estimated arrival time and optimal route) to the selected means of transport using the instruction mechanism.

[0461] (Example 1)

[0462] 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."

[0463] To achieve efficient use of transportation and improve user satisfaction, accurate selection of transportation methods based on user requests and optimal allocation according to demand are necessary. However, previous systems have struggled to analyze user requests in real time and provide efficient transportation methods. In particular, there has been a problem in that the rapid selection of transportation methods, taking into account traffic conditions and user preferences, has not been adequately performed.

[0464] 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.

[0465] In this invention, the server includes information analysis means, a learning device, and a request prediction device. This enables the selection of the optimal means of transport based on the user's geographical location, destination, and preferences, and the optimal allocation of means of transport according to demand.

[0466] "Information analysis means" refers to technology that analyzes the user's geographical location and destination information to select the appropriate means of transportation.

[0467] A "learning device" is a machine that learns patterns based on the user's past usage history and preferences, and uses this information to select the appropriate means of transport.

[0468] A "demand forecasting device" is a mechanism that forecasts demand in each region and optimally allocates transportation methods.

[0469] A "communication device" is a device that provides selected means of transport and optimized route information based on information received from the user's terminal.

[0470] An "instruction device" is a machine that provides detailed operational instructions to a selected means of transport.

[0471] This system aims to optimize transportation by effectively managing transportation methods using information analysis tools, learning devices, and request prediction devices. It primarily revolves around three components: a server, terminals, and users, all working together to function.

[0472] The server collects and analyzes geographic location and destination data sent from each user as a means of information analysis. Databases and data analysis software are used for this process. The technologies employed include, for example, Python libraries and analysis algorithms using the R language. Furthermore, a generative AI model is employed to learn from past travel history, utilizing scikit-learn and TensorFlow to thoroughly learn user patterns.

[0473] The terminal provides an interface for user interaction. When a user enters a ride request, the terminal quickly transmits that information to the server. The information transmitted from the terminal is processed by the server in real time, and the results are fed back to the user. This communication utilizes common mobile communication technologies and internet protocols.

[0474] Through a terminal application, users specify their current location, destination, and special requirements (e.g., "quiet interior" or "highly-rated driver"). This allows the system to select the most suitable mode of transportation based on the user's needs. A concrete example is when a user plans to travel to a meeting within the city; the system suggests appropriate taxis or rideshare services.

[0475] For demand forecasting, the server performs real-time traffic analysis and area-specific demand analysis. In some cases, an automated geographic information system (GIS) is used. Based on the obtained data, the server runs a model to most efficiently allocate transportation methods, enabling a rapid response.

[0476] A concrete example of a prompt is the input, "Please suggest the best mode of transportation based on the user's current location, destination, and preferences." This allows the generative AI model to perform appropriate data analysis and provide the most satisfying mode of transportation for the user.

[0477] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0478] Step 1:

[0479] The user enters their ride request using a terminal application. The user enters their current location, destination, and desired conditions (e.g., quiet interior, specific vehicle type). This information is entered through the terminal's UI and prepared as data in JSON format. The terminal then prepares to send this data to the server. The entered information embodies the user's travel request.

[0480] Step 2:

[0481] The terminal transmits JSON-formatted data obtained from the user to the server in real time. This transmission utilizes mobile networks and internet communication, and is conducted via the HTTP protocol. This ensures that user requests are quickly and reliably transmitted to the server. The terminal's output is user request information encoded in a format that the server can parse.

[0482] Step 3:

[0483] The server analyzes the received data. For analysis, the server uses a generative AI model and analyzes user data, past usage history, and current traffic information. Specifically, it uses data analysis tools such as Python and R. The input data is clustered and categorized to determine which mode of transport is optimal. This process generates an output that selects the most suitable mode of transport for the user.

[0484] Step 4:

[0485] The server performs demand forecasting. Real-time geographic and traffic data are used for demand forecasting. The server utilizes GIS tools to predict demand in a specific area and calculates the optimal placement of transportation methods based on that forecast. The input is current geographic and traffic data, and the output is a guideline for transportation placement.

[0486] Step 5:

[0487] The server transmits real-time information to the terminal, including optimal transportation methods and route information. This information includes the selected transportation method, recommended route, and estimated arrival time. The HTTP protocol is again used for this communication, and real-time operation is required. The server then performs a process of providing the selection information to the user as output.

[0488] Step 6:

[0489] The terminal displays information received from the server to the user. The user reviews this information and, if they agree to the proposed transportation method and fee, accepts it by clicking the "Confirm" button. The input is the transportation method information from the server, and the output is the data that is resent to the server as the user's consent or request for correction.

[0490] Step 7:

[0491] The server, upon user confirmation, sends instructions to the transportation provider. These instructions include the planned route, recommended routes, and any special requests. The details are transmitted to the relevant transportation service via the server's communication equipment. As a result of these instructions, the selected transportation provider can quickly head to the user's location.

[0492] (Application Example 1)

[0493] 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."

[0494] In modern society, there is a demand for efficient and customized means of transportation. However, challenges remain in selecting the optimal mode of transport to suit the diverse preferences of each user and providing real-time monitoring of transportation information. Therefore, improving convenience during travel and enhancing the user experience are crucial.

[0495] 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.

[0496] In this invention, the server includes data analysis means for selecting the optimal mode of transport based on the user's geographical location and destination; learning means for learning the user's preferences and reflecting them in the selection of the mode of transport; and communication means for transmitting real-time operational information of the mode of transport to the user terminal and requesting confirmation. As a result, the user can select the optimal mode of transport according to their preferences in real time and visually monitor the operational status on their terminal.

[0497] "Means of transportation" refers to all means of transporting users from one point to another.

[0498] "User" refers to an individual or group that wishes to travel using a means of transportation.

[0499] "Geographic location" refers to specific geographical coordinates obtained using the device's location information function.

[0500] "Destination" refers to the point designated by the user as the final stop of their journey.

[0501] "Transportation means" refers to the specific means of transport or devices used to achieve movement.

[0502] "Data analysis means" refers to devices or software that analyze the user's geographical location and destination information and select the optimal means of transportation.

[0503] "Learning tools" refer to devices or software that accumulate data on a user's past behavior and preferences, and use that data to select the most suitable mode of transportation for that user.

[0504] "Demand forecasting tools" refer to devices or software that analyze past usage patterns of transportation methods and current conditions to predict future demand for transportation methods.

[0505] "Instruction means" refers to devices or software that have the function of providing detailed operational instructions and recommended routes for selected means of transport.

[0506] "Communication means" refers to devices or software that have the function of sending and receiving data bidirectionally between a server and a user's terminal.

[0507] "Display means" refers to devices or software that have the function of visually presenting information to the user on a terminal.

[0508] The system that realizes this invention consists of three main elements: a server, a terminal, and a user. The server is built using programming languages ​​such as Python or Java and is operated on hardware capable of processing large amounts of data at high speed. Machine learning libraries such as Scikit-learn and TensorFlow are used and applied to data analysis and demand forecasting.

[0509] First, the user enters their ride request using a terminal. They enter their current location, destination, and desired conditions (such as a quiet vehicle or a specific mode of transport) through the terminal's user interface. The entered information is processed by an application installed on the terminal and sent to the server.

[0510] The server analyzes the received information and selects the optimal mode of transport. In this process, the server learns the user's individual transport preferences based on their past usage history. It also analyzes real-time traffic conditions and local demand forecasts to determine the optimal vehicle allocation. The server aggregates this information and transmits it to the terminal via communication, along with optimized route information.

[0511] The terminal displays the received information to the user, visually showing the transportation details. This allows the user to easily check the recommended mode of transport and its details. Once the confirmation is complete, the selected mode of transport is arranged.

[0512] For example, suppose a user is planning a trip to a new shopping mall and desires a highly-rated driver and a quiet ride. In this case, the server considers the user's past travel history and current traffic conditions to instantly select a suitable autonomous vehicle and suggest it to the user's device. The user can then check the vehicle's estimated arrival time in real time on their device.

[0513] Example prompt message: "Please specify your destination. Do you prefer a quiet ride? The best route and vehicle will be suggested considering current traffic conditions."

[0514] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0515] Step 1:

[0516] The user enters their ride request using a terminal. The information entered includes their current location, destination, and preferred conditions (e.g., quiet interior). This information is collected by the terminal's interface and formatted as digital data. This data is then converted to JSON format and prepared for transmission to the server.

[0517] Step 2:

[0518] The terminal sends the data entered by the user to the server. This data is sent to the server via a stable network using a communication method. The transmitted data undergoes initial data analysis processing on the server.

[0519] Step 3:

[0520] The server performs data analysis based on the received user data. Specifically, geographical location, destination, and the user's past usage history are retrieved from the database, and this information is used to select the optimal mode of transport. This analysis is performed using Scikit-learn or TensorFlow. The output is a list of optimized modes of transport.

[0521] Step 4:

[0522] The server uses demand forecasting methods to analyze traffic conditions in real time. This involves using historical travel patterns and current traffic data. The server integrates these results to determine the optimal deployment of transportation methods. As a result, optimal route information is generated.

[0523] Step 5:

[0524] The server transmits the selected means of transport and route information to the terminal. This information is transmitted quickly using communication methods.

[0525] Step 6:

[0526] The terminal visually displays information received from the server to the user. The user interface clearly shows the user operational information and recommended modes of transport. This allows the user to confirm their boarding based on the information presented.

[0527] Step 7:

[0528] After the user reviews the presented information, they perform a "confirmation" operation through their device. Based on the user's consent, the device notifies the server. The server receives this notification and makes the final arrangements for the selected means of transport.

[0529] 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.

[0530] This invention is a system that combines the optimization of conventional means of transportation with an emotion engine that recognizes the user's emotional state. This system consists of three main components: a server, a terminal, and a user.

[0531] First, the user enters their ride request using a terminal application. This typically includes their geographical location and destination, as well as their preferences (vehicle type, quiet environment, etc.). The terminal is also equipped with a camera and microphone to capture the user's facial expressions and voice. This data is analyzed by an emotion engine to recognize the user's emotional state (e.g., stress, anxiety, joy).

[0532] The recognized emotion data, along with other request information, is sent to the server in real time. The server uses data analysis tools to comprehensively analyze the user's request, past usage history, and emotional state. The emotional state analyzed by the emotion engine is reflected in the transportation selection process. For example, if the user is feeling stressed, the system prioritizes selecting a transportation method that provides a more comfortable in-vehicle environment.

[0533] Furthermore, the server uses demand forecasting tools to predict local demand and, while also considering the emotional state of users, determines the optimal placement of transportation methods. The selected transportation methods and optimal route information, transmitted through the instruction tools, are sent to the user's terminal and also notified to the transportation methods.

[0534] The terminal presents the user with information on transportation options, fares, and emotionally-based service adjustments received from the server. Once the user confirms this, the arrangement of the selected transportation method is completed. Through this process, users can enjoy a travel experience tailored to their emotional state.

[0535] For example, if a user is feeling stressed on their way to an important presentation, the emotion engine detects this state, and the server prioritizes selecting a vehicle with a quiet environment and a highly-rated driver. This allows the user to reach their destination in a calm state. In this way, the present invention realizes the provision of new value that takes the user's emotions into consideration.

[0536] The following describes the processing flow.

[0537] Step 1:

[0538] The user opens a terminal application and enters their current location, destination, and preferred ride conditions. Emotional data is collected from the user's facial expressions and voice using the camera and microphone.

[0539] Step 2:

[0540] The device sends the information entered by the user and the collected sentiment data to the server.

[0541] Step 3:

[0542] The server processes the user request information it receives using data analysis tools and retrieves past usage history and preference data from a database.

[0543] Step 4:

[0544] The server's emotion engine analyzes the user's facial expressions and voice data to identify the user's emotional state. For example, it might analyze that the user is feeling stressed.

[0545] Step 5:

[0546] Based on the analysis results, the server selects a mode of transportation that matches the user's emotional state and preferences. For example, it might prioritize vehicles with a quiet environment to reduce stress.

[0547] Step 6:

[0548] The server analyzes local demand using demand forecasting tools and plans the optimal allocation of transportation methods. It also takes emotional states into consideration to enable more comfortable choices.

[0549] Step 7:

[0550] The server sends the selected transportation method, calculated optimal route, fare information, and emotion-based service recommendations to the user's terminal.

[0551] Step 8:

[0552] The terminal displays all information received from the server to the user and requests final confirmation.

[0553] Step 9:

[0554] The user is satisfied with the information presented and taps the confirmation button. The confirmation is sent to the server.

[0555] Step 10:

[0556] Once the server receives user confirmation, it uses the instruction mechanism to send action instructions to the selected means of transport. The means of transport is notified of the optimal route and estimated arrival time.

[0557] Step 11:

[0558] Once the user's ride is complete, the data obtained by the emotion engine is saved to a database for further optimization in future rides.

[0559] (Example 2)

[0560] 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."

[0561] When users utilize transportation, they need to select the optimal mode of transport not only based on geographical factors and mere preferences, but also taking into account their emotional state at the time. However, conventional systems have failed to adequately reflect users' emotional states, sometimes leading to decreased satisfaction. Therefore, improving the quality of the user's travel experience and reducing stress is a challenge.

[0562] 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.

[0563] In this invention, the server includes information processing means, learning means, prediction means, analysis means, and control means. This makes it possible to comprehensively analyze the user's geographical information, preferences, and emotional state, and select the optimal means of transportation.

[0564] "Information processing means" refers to the processing performed within a system to select the optimal means of transportation based on the user's geographical location information and destination information.

[0565] A "learning method" is a function that establishes a process for determining the most suitable mode of transportation for a user, based on data regarding the user's preferences and emotional state.

[0566] A "predictive method" is a technique used to predict demand and determine the optimal allocation of transportation methods based on past usage trends of transportation methods.

[0567] "Analysis means" refers to a process that analyzes the user's voice and facial expression data and has the function of evaluating their emotional state.

[0568] "Control means" refers to a function within the system that provides instructions to the selected means of transport and ensures the smooth execution of the actual transport process.

[0569] The present invention provides a means of transportation that takes into account the user's geographical information, preferences, and emotional state in order to optimize the user's movement. This system mainly consists of three main components: a server, a terminal, and a user.

[0570] Users make travel requests using a terminal application. This application is downloaded to smartphones and tablets and accepts user input. The information collected includes the current location, destination, and transportation preferences (e.g., luxury car, quiet environment). In addition, the terminal uses a camera and microphone to capture the user's facial expressions and voice data.

[0571] The data collected by the device is transmitted in real time to a server within the system. On the server, an emotion analysis algorithm processes this data and evaluates the user's emotional state (e.g., stress, anxiety, joy). In this emotion analysis, existing machine learning models are utilized.

[0572] The server uses information processing tools to comprehensively analyze user request information, sentiment analysis results, and past travel history data. This allows for the selection of the most suitable mode of transportation for each user. Furthermore, demand forecasting models are used to predict regional demand, enabling the optimal allocation of transportation. The selected mode of transportation and route information are transmitted from the server to the terminal and presented. Finally, the user confirms this information, and the selected arrangements are completed.

[0573] For example, if a user is experiencing high levels of stress during their commute, the emotion engine immediately recognizes this state and selects a mode of transportation that provides a quieter and more relaxing environment. In this way, the system provides a comfortable travel service based on the user's emotions.

[0574] An example of a prompt message generated using an AI model is, "If the user is perceived as feeling stressed, please suggest the most suitable mode of transportation."

[0575] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0576] Step 1:

[0577] The user launches the terminal application and enters a ride request. The input data includes the current location (GPS data), destination, and preferred conditions for the ride (e.g., quiet environment, specific vehicle type). This information is organized and prepared in the appropriate format by the terminal. The output is a ride request information packet.

[0578] Step 2:

[0579] The device uses a camera and microphone to capture the user's facial expressions and voice. This data is processed in real time and generated as input data to determine the user's emotional state. The output consists of audio and video data for emotion analysis.

[0580] Step 3:

[0581] The server receives boarding request information packets and audio / video data for emotion analysis transmitted from the terminal. The server executes an emotion analysis algorithm to evaluate the user's emotional state. The output obtained from this process is evaluation information indicating the user's emotional state.

[0582] Step 4:

[0583] The server uses information processing tools to integrate ride request information, emotional state evaluation information, and past usage history data to select the optimal mode of transportation. A pattern recognition algorithm is used for comprehensive analysis. The output is a customized proposal for transportation for each user.

[0584] Step 5:

[0585] The server uses demand forecasting tools to predict regional demand. The server runs a forecasting model based on historical movement data to determine the optimal placement. The output of this process is information on the placement of transportation methods based on the forecast.

[0586] Step 6:

[0587] The terminal displays suggested transportation options received from the server to the user. This information includes details of the transportation method, fares, and route options. An interface is provided for the user to review and make a selection. The output is the transportation information presented to the user.

[0588] Step 7:

[0589] The user makes a selection based on the provided information. They then perform an action to notify the server that arrangements for the selected mode of transport are complete. This completes the system-wide operation. The output is information about the final decided mode of transport.

[0590] (Application Example 2)

[0591] 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."

[0592] Traditional transportation selection is based solely on the user's geographical location and destination, making it difficult to provide services tailored to the individual user's emotional state. This has resulted in situations where, even when users are stressed or experiencing specific emotional states, the appropriate travel experience cannot be provided. Therefore, there is a need to recognize each user's emotional state and select the most suitable mode of transportation accordingly, thereby providing a more personalized travel experience.

[0593] 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.

[0594] In this invention, the server includes information analysis means, learning means, demand forecasting means, and sentiment analysis means. This makes it possible to optimize the means of transportation by taking into account the user's geographical location and destination, as well as their emotional state at that time.

[0595] "Information analysis means" refers to a function that analyzes data such as the user's geographical location, destination, and past usage history in order to select the most suitable means of transportation.

[0596] A "learning tool" is a function that takes into account the user's preferences and past choices to inform the selection of the means of transportation.

[0597] A "demand forecasting tool" is a function that predicts the demand for transportation based on past usage patterns of modes of transport.

[0598] A "controlling mechanism" is a function that instructs the user on how the selected means of transport should operate.

[0599] "Emotional analysis tools" are functions that recognize the user's emotional state and adjust the selection of transportation methods based on that.

[0600] In the embodiments of this invention, the system is implemented using three main components: a user, a terminal, and a server.

[0601] ■System Configuration

[0602] Users utilize smartphones or smart glasses equipped with cameras and microphones. These devices capture the user's facial expressions and voice, and transmit them to an emotion analysis system. This system uses software called an emotion engine to analyze the user's emotional state.

[0603] ■Data Processing

[0604] The server processes data as an information analysis tool. It comprehensively analyzes the user's geographical location, destination, past usage history, and emotional state to select the optimal mode of transport and route. In this process, a generative AI model is used to enhance route suggestions based on the user's emotional state. The selected mode of transport and route information is transmitted to the user's terminal via an instruction device.

[0605] For example, if a user is in a highly stressed state, the emotion analysis system will detect this state, and the server will prioritize selecting a quiet and calming mode of transport. Relaxing music will be played during transport.

[0606] By providing the system with a prompt such as, "Please suggest the optimal travel route for this user to relax. The user is feeling stressed," the system's emotion analysis and information analysis capabilities can work together to provide the most suitable service.

[0607] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0608] Step 1:

[0609] The user uses a smartphone or smart glasses to input their ride request into a terminal application. During this process, the terminal captures information such as geographical location, destination, and preferred vehicle type, as well as the user's facial expressions and voice. The terminal then transmits the captured data to a sentiment analysis system.

[0610] Step 2:

[0611] The device uses an emotion engine to analyze the user's emotional state using captured facial expressions and voice data as input. Specifically, it recognizes the user's emotional state, such as stress, anxiety, and joy, from this data and outputs the result. The emotional state data is sent to a server.

[0612] Step 3:

[0613] The server receives geographical location, destination, user's past usage history, and emotional state as input for information analysis. This data is integrated, and a generative AI model is used to calculate and output the optimal mode of transport and route. During this process, the user's emotional state is taken into consideration when selecting the mode of transport.

[0614] Step 4:

[0615] The server uses demand forecasting tools to predict the demand for transportation methods across the entire region. Using past travel patterns as input, it determines and outputs the optimal allocation of transportation methods that are appropriate to the current situation.

[0616] Step 5:

[0617] The server transmits the selected mode of transport and route information to the user's terminal via a command system. During transport, suggestions are made to provide the user with a more comfortable travel experience (e.g., playing relaxing music).

[0618] Step 6:

[0619] Users review information about the received transportation method and experience the provided service. If necessary, they can submit feedback via their device, and data is collected to help improve future use.

[0620] Through the above processing flow, users can enjoy an optimal travel experience tailored to their emotional state.

[0621] 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.

[0622] 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.

[0623] 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.

[0624] [Fourth Embodiment]

[0625] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0626] 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.

[0627] 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).

[0628] 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.

[0629] 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.

[0630] 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).

[0631] 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.

[0632] 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.

[0633] 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.

[0634] 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.

[0635] 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.

[0636] 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.

[0637] 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".

[0638] This invention provides a system for optimizing means of transportation. The system consists of three main components: a server, a terminal, and a user.

[0639] First, the user enters a ride request using a terminal application. The user can specify their current location and destination, and enter their preferred conditions (for example, a quiet interior or a specific vehicle type). The user's input information is sent to the server in real time.

[0640] The server has data analysis tools designed to analyze geographical location and destination data provided by users, as well as past usage history and preferences. A learning tool on the server records each user's past travel history and learns the patterns. Based on this information, it selects a mode of transportation that matches the user's preferences.

[0641] Furthermore, the server uses demand forecasting tools to analyze current traffic conditions and area demand in real time. Based on this analysis, it determines the optimal allocation of transportation. For example, if demand is high in a particular area, transportation will be concentrated in that area.

[0642] The selected mode of transport and optimized route information are transmitted from the server to the user's terminal. The server also provides detailed operational instructions (such as estimated arrival time and recommended route) to the selected mode of transport using an instruction mechanism.

[0643] The terminal displays information sent from the server to the user and requests confirmation. If the user is satisfied with the presented transportation method and fare information, selecting "Confirm" completes the arrangement of transportation. Through this procedure, users can travel to their destination efficiently without waiting time.

[0644] As a concrete example, suppose a user wants to travel to a meeting within the city and prefers a highly-rated driver and a comfortable ride. In this case, the server analyzes past patterns and current traffic conditions to instantly select and provide a mode of transportation that matches the user's preferences. This process ensures that the user enjoys a satisfying travel experience.

[0645] The following describes the processing flow.

[0646] Step 1:

[0647] The user opens the terminal application and enters their current location, destination, and ride preferences. This information is then sent from the terminal to the server.

[0648] Step 2:

[0649] The server receives information from the user and uses data analysis tools to retrieve the user's past usage history and preferences from the database.

[0650] Step 3:

[0651] The server uses learning mechanisms to select a transportation method that best suits the user's preferences based on user input and past history.

[0652] Step 4:

[0653] The server uses demand forecasting tools to analyze the demand across the entire region in real time and optimize the allocation of selected transportation methods.

[0654] Step 5:

[0655] The server sends the selected transportation method, optimal route, and pre-calculated fare to the user's terminal.

[0656] Step 6:

[0657] The terminal displays information received from the server to the user, and the user confirms the presented information.

[0658] Step 7:

[0659] If the user is satisfied with the proposed transportation method and price, they tap the "Confirm" button. This confirmation is then sent from the device to the server.

[0660] Step 8:

[0661] The server receives user confirmation and sends action instructions (estimated arrival time and optimal route) to the selected means of transport using the instruction mechanism.

[0662] (Example 1)

[0663] 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".

[0664] To achieve efficient use of transportation and improve user satisfaction, accurate selection of transportation methods based on user requests and optimal allocation according to demand are necessary. However, previous systems have struggled to analyze user requests in real time and provide efficient transportation methods. In particular, there has been a problem in that the rapid selection of transportation methods, taking into account traffic conditions and user preferences, has not been adequately performed.

[0665] 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.

[0666] In this invention, the server includes information analysis means, a learning device, and a request prediction device. This enables the selection of the optimal means of transport based on the user's geographical location, destination, and preferences, and the optimal allocation of means of transport according to demand.

[0667] "Information analysis means" refers to technology that analyzes the user's geographical location and destination information to select the appropriate means of transportation.

[0668] A "learning device" is a machine that learns patterns based on the user's past usage history and preferences, and uses this information to select the appropriate means of transport.

[0669] A "demand forecasting device" is a mechanism that forecasts demand in each region and optimally allocates transportation methods.

[0670] A "communication device" is a device that provides selected means of transport and optimized route information based on information received from the user's terminal.

[0671] An "instruction device" is a machine that provides detailed operational instructions to a selected means of transport.

[0672] This system aims to optimize transportation by effectively managing transportation methods using information analysis tools, learning devices, and request prediction devices. It primarily revolves around three components: a server, terminals, and users, all working together to function.

[0673] The server collects and analyzes geographic location and destination data sent from each user as a means of information analysis. Databases and data analysis software are used for this process. The technologies employed include, for example, Python libraries and analysis algorithms using the R language. Furthermore, a generative AI model is employed to learn from past travel history, utilizing scikit-learn and TensorFlow to thoroughly learn user patterns.

[0674] The terminal provides an interface for user interaction. When a user enters a ride request, the terminal quickly transmits that information to the server. The information transmitted from the terminal is processed by the server in real time, and the results are fed back to the user. This communication utilizes common mobile communication technologies and internet protocols.

[0675] Through a terminal application, users specify their current location, destination, and special requirements (e.g., "quiet interior" or "highly-rated driver"). This allows the system to select the most suitable mode of transportation based on the user's needs. A concrete example is when a user plans to travel to a meeting within the city; the system suggests appropriate taxis or rideshare services.

[0676] For demand forecasting, the server performs real-time traffic analysis and area-specific demand analysis. In some cases, an automated geographic information system (GIS) is used. Based on the obtained data, the server runs a model to most efficiently allocate transportation methods, enabling a rapid response.

[0677] A concrete example of a prompt is the input, "Please suggest the best mode of transportation based on the user's current location, destination, and preferences." This allows the generative AI model to perform appropriate data analysis and provide the most satisfying mode of transportation for the user.

[0678] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0679] Step 1:

[0680] The user enters their ride request using a terminal application. The user enters their current location, destination, and desired conditions (e.g., quiet interior, specific vehicle type). This information is entered through the terminal's UI and prepared as data in JSON format. The terminal then prepares to send this data to the server. The entered information embodies the user's travel request.

[0681] Step 2:

[0682] The terminal transmits JSON-formatted data obtained from the user to the server in real time. This transmission utilizes mobile networks and internet communication, and is conducted via the HTTP protocol. This ensures that user requests are quickly and reliably transmitted to the server. The terminal's output is user request information encoded in a format that the server can parse.

[0683] Step 3:

[0684] The server analyzes the received data. For analysis, the server uses a generative AI model and analyzes user data, past usage history, and current traffic information. Specifically, it uses data analysis tools such as Python and R. The input data is clustered and categorized to determine which mode of transport is optimal. This process generates an output that selects the most suitable mode of transport for the user.

[0685] Step 4:

[0686] The server performs demand forecasting. Real-time geographic and traffic data are used for demand forecasting. The server utilizes GIS tools to predict demand in a specific area and calculates the optimal placement of transportation methods based on that forecast. The input is current geographic and traffic data, and the output is a guideline for transportation placement.

[0687] Step 5:

[0688] The server transmits real-time information to the terminal, including optimal transportation methods and route information. This information includes the selected transportation method, recommended route, and estimated arrival time. The HTTP protocol is again used for this communication, and real-time operation is required. The server then performs a process of providing the selection information to the user as output.

[0689] Step 6:

[0690] The terminal displays information received from the server to the user. The user reviews this information and, if they agree to the proposed transportation method and fee, accepts it by clicking the "Confirm" button. The input is the transportation method information from the server, and the output is the data that is resent to the server as the user's consent or request for correction.

[0691] Step 7:

[0692] The server, upon user confirmation, sends instructions to the transportation provider. These instructions include the planned route, recommended routes, and any special requests. The details are transmitted to the relevant transportation service via the server's communication equipment. As a result of these instructions, the selected transportation provider can quickly head to the user's location.

[0693] (Application Example 1)

[0694] 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".

[0695] In modern society, there is a demand for efficient and customized means of transportation. However, challenges remain in selecting the optimal mode of transport to suit the diverse preferences of each user and providing real-time monitoring of transportation information. Therefore, improving convenience during travel and enhancing the user experience are crucial.

[0696] 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.

[0697] In this invention, the server includes data analysis means for selecting the optimal mode of transport based on the user's geographical location and destination; learning means for learning the user's preferences and reflecting them in the selection of the mode of transport; and communication means for transmitting real-time operational information of the mode of transport to the user terminal and requesting confirmation. As a result, the user can select the optimal mode of transport according to their preferences in real time and visually monitor the operational status on their terminal.

[0698] "Means of transportation" refers to all means of transporting users from one point to another.

[0699] "User" refers to an individual or group that wishes to travel using a means of transportation.

[0700] "Geographic location" refers to specific geographical coordinates obtained using the device's location information function.

[0701] "Destination" refers to the point designated by the user as the final stop of their journey.

[0702] "Transportation means" refers to the specific means of transport or devices used to achieve movement.

[0703] "Data analysis means" refers to devices or software that analyze the user's geographical location and destination information and select the optimal means of transportation.

[0704] "Learning tools" refer to devices or software that accumulate data on a user's past behavior and preferences, and use that data to select the most suitable mode of transportation for that user.

[0705] "Demand forecasting tools" refer to devices or software that analyze past usage patterns of transportation methods and current conditions to predict future demand for transportation methods.

[0706] "Instruction means" refers to devices or software that have the function of providing detailed operational instructions and recommended routes for selected means of transport.

[0707] "Communication means" refers to devices or software that have the function of sending and receiving data bidirectionally between a server and a user's terminal.

[0708] "Display means" refers to devices or software that have the function of visually presenting information to the user on a terminal.

[0709] The system that realizes this invention consists of three main elements: a server, a terminal, and a user. The server is built using programming languages ​​such as Python or Java and is operated on hardware capable of processing large amounts of data at high speed. Machine learning libraries such as Scikit-learn and TensorFlow are used and applied to data analysis and demand forecasting.

[0710] First, the user enters their ride request using a terminal. They enter their current location, destination, and desired conditions (such as a quiet vehicle or a specific mode of transport) through the terminal's user interface. The entered information is processed by an application installed on the terminal and sent to the server.

[0711] The server analyzes the received information and selects the optimal mode of transport. In this process, the server learns the user's individual transport preferences based on their past usage history. It also analyzes real-time traffic conditions and local demand forecasts to determine the optimal vehicle allocation. The server aggregates this information and transmits it to the terminal via communication, along with optimized route information.

[0712] The terminal displays the received information to the user, visually showing the transportation details. This allows the user to easily check the recommended mode of transport and its details. Once the confirmation is complete, the selected mode of transport is arranged.

[0713] For example, suppose a user is planning a trip to a new shopping mall and desires a highly-rated driver and a quiet ride. In this case, the server considers the user's past travel history and current traffic conditions to instantly select a suitable autonomous vehicle and suggest it to the user's device. The user can then check the vehicle's estimated arrival time in real time on their device.

[0714] Example prompt message: "Please specify your destination. Do you prefer a quiet ride? The best route and vehicle will be suggested considering current traffic conditions."

[0715] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0716] Step 1:

[0717] The user enters their ride request using a terminal. The information entered includes their current location, destination, and preferred conditions (e.g., quiet interior). This information is collected by the terminal's interface and formatted as digital data. This data is then converted to JSON format and prepared for transmission to the server.

[0718] Step 2:

[0719] The terminal sends the data entered by the user to the server. This data is sent to the server via a stable network using a communication method. The transmitted data undergoes initial data analysis processing on the server.

[0720] Step 3:

[0721] The server performs data analysis based on the received user data. Specifically, geographical location, destination, and the user's past usage history are retrieved from the database, and this information is used to select the optimal mode of transport. This analysis is performed using Scikit-learn or TensorFlow. The output is a list of optimized modes of transport.

[0722] Step 4:

[0723] The server uses demand forecasting methods to analyze traffic conditions in real time. This involves using historical travel patterns and current traffic data. The server integrates these results to determine the optimal deployment of transportation methods. As a result, optimal route information is generated.

[0724] Step 5:

[0725] The server transmits the selected means of transport and route information to the terminal. This information is transmitted quickly using communication methods.

[0726] Step 6:

[0727] The terminal visually displays information received from the server to the user. The user interface clearly shows the user operational information and recommended modes of transport. This allows the user to confirm their boarding based on the information presented.

[0728] Step 7:

[0729] After the user reviews the presented information, they perform a "confirmation" operation through their device. Based on the user's consent, the device notifies the server. The server receives this notification and makes the final arrangements for the selected means of transport.

[0730] 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.

[0731] This invention is a system that combines the optimization of conventional means of transportation with an emotion engine that recognizes the user's emotional state. This system consists of three main components: a server, a terminal, and a user.

[0732] First, the user enters their ride request using a terminal application. This typically includes their geographical location and destination, as well as their preferences (vehicle type, quiet environment, etc.). The terminal is also equipped with a camera and microphone to capture the user's facial expressions and voice. This data is analyzed by an emotion engine to recognize the user's emotional state (e.g., stress, anxiety, joy).

[0733] The recognized emotion data, along with other request information, is sent to the server in real time. The server uses data analysis tools to comprehensively analyze the user's request, past usage history, and emotional state. The emotional state analyzed by the emotion engine is reflected in the transportation selection process. For example, if the user is feeling stressed, the system prioritizes selecting a transportation method that provides a more comfortable in-vehicle environment.

[0734] Furthermore, the server uses demand forecasting tools to predict local demand and, while also considering the emotional state of users, determines the optimal placement of transportation methods. The selected transportation methods and optimal route information, transmitted through the instruction tools, are sent to the user's terminal and also notified to the transportation methods.

[0735] The terminal presents the user with information on transportation options, fares, and emotionally-based service adjustments received from the server. Once the user confirms this, the arrangement of the selected transportation method is completed. Through this process, users can enjoy a travel experience tailored to their emotional state.

[0736] For example, if a user is feeling stressed on their way to an important presentation, the emotion engine detects this state, and the server prioritizes selecting a vehicle with a quiet environment and a highly-rated driver. This allows the user to reach their destination in a calm state. In this way, the present invention realizes the provision of new value that takes the user's emotions into consideration.

[0737] The following describes the processing flow.

[0738] Step 1:

[0739] The user opens a terminal application and enters their current location, destination, and preferred ride conditions. Emotional data is collected from the user's facial expressions and voice using the camera and microphone.

[0740] Step 2:

[0741] The device sends the information entered by the user and the collected sentiment data to the server.

[0742] Step 3:

[0743] The server processes the user request information it receives using data analysis tools and retrieves past usage history and preference data from a database.

[0744] Step 4:

[0745] The server's emotion engine analyzes the user's facial expressions and voice data to identify the user's emotional state. For example, it might analyze that the user is feeling stressed.

[0746] Step 5:

[0747] Based on the analysis results, the server selects a mode of transportation that matches the user's emotional state and preferences. For example, it might prioritize vehicles with a quiet environment to reduce stress.

[0748] Step 6:

[0749] The server analyzes local demand using demand forecasting tools and plans the optimal allocation of transportation methods. It also takes emotional states into consideration to enable more comfortable choices.

[0750] Step 7:

[0751] The server sends the selected transportation method, calculated optimal route, fare information, and emotion-based service recommendations to the user's terminal.

[0752] Step 8:

[0753] The terminal displays all information received from the server to the user and requests final confirmation.

[0754] Step 9:

[0755] The user is satisfied with the information presented and taps the confirmation button. The confirmation is sent to the server.

[0756] Step 10:

[0757] Once the server receives user confirmation, it uses the instruction mechanism to send action instructions to the selected means of transport. The means of transport is notified of the optimal route and estimated arrival time.

[0758] Step 11:

[0759] Once the user's ride is complete, the data obtained by the emotion engine is saved to a database for further optimization in future rides.

[0760] (Example 2)

[0761] 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".

[0762] When users utilize transportation, they need to select the optimal mode of transport not only based on geographical factors and mere preferences, but also taking into account their emotional state at the time. However, conventional systems have failed to adequately reflect users' emotional states, sometimes leading to decreased satisfaction. Therefore, improving the quality of the user's travel experience and reducing stress is a challenge.

[0763] 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.

[0764] In this invention, the server includes information processing means, learning means, prediction means, analysis means, and control means. This makes it possible to comprehensively analyze the user's geographical information, preferences, and emotional state, and select the optimal means of transportation.

[0765] "Information processing means" refers to the processing performed within a system to select the optimal means of transportation based on the user's geographical location information and destination information.

[0766] A "learning method" is a function that establishes a process for determining the most suitable mode of transportation for a user, based on data regarding the user's preferences and emotional state.

[0767] A "predictive method" is a technique used to predict demand and determine the optimal allocation of transportation methods based on past usage trends of transportation methods.

[0768] "Analysis means" refers to a process that analyzes the user's voice and facial expression data and has the function of evaluating their emotional state.

[0769] "Control means" refers to a function within the system that provides instructions to the selected means of transport and ensures the smooth execution of the actual transport process.

[0770] The present invention provides a means of transportation that takes into account the user's geographical information, preferences, and emotional state in order to optimize the user's movement. This system mainly consists of three main components: a server, a terminal, and a user.

[0771] Users make travel requests using a terminal application. This application is downloaded to smartphones and tablets and accepts user input. The information collected includes the current location, destination, and transportation preferences (e.g., luxury car, quiet environment). In addition, the terminal uses a camera and microphone to capture the user's facial expressions and voice data.

[0772] The data collected by the device is transmitted in real time to a server within the system. On the server, an emotion analysis algorithm processes this data and evaluates the user's emotional state (e.g., stress, anxiety, joy). In this emotion analysis, existing machine learning models are utilized.

[0773] The server uses information processing tools to comprehensively analyze user request information, sentiment analysis results, and past travel history data. This allows for the selection of the most suitable mode of transportation for each user. Furthermore, demand forecasting models are used to predict regional demand, enabling the optimal allocation of transportation. The selected mode of transportation and route information are transmitted from the server to the terminal and presented. Finally, the user confirms this information, and the selected arrangements are completed.

[0774] For example, if a user is experiencing high levels of stress during their commute, the emotion engine immediately recognizes this state and selects a mode of transportation that provides a quieter and more relaxing environment. In this way, the system provides a comfortable travel service based on the user's emotions.

[0775] An example of a prompt message generated using an AI model is, "If the user is perceived as feeling stressed, please suggest the most suitable mode of transportation."

[0776] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0777] Step 1:

[0778] The user launches the terminal application and enters a ride request. The input data includes the current location (GPS data), destination, and preferred conditions for the ride (e.g., quiet environment, specific vehicle type). This information is organized and prepared in the appropriate format by the terminal. The output is a ride request information packet.

[0779] Step 2:

[0780] The device uses a camera and microphone to capture the user's facial expressions and voice. This data is processed in real time and generated as input data to determine the user's emotional state. The output consists of audio and video data for emotion analysis.

[0781] Step 3:

[0782] The server receives boarding request information packets and audio / video data for emotion analysis transmitted from the terminal. The server executes an emotion analysis algorithm to evaluate the user's emotional state. The output obtained from this process is evaluation information indicating the user's emotional state.

[0783] Step 4:

[0784] The server uses information processing tools to integrate ride request information, emotional state evaluation information, and past usage history data to select the optimal mode of transportation. A pattern recognition algorithm is used for comprehensive analysis. The output is a customized proposal for transportation for each user.

[0785] Step 5:

[0786] The server uses demand forecasting tools to predict regional demand. The server runs a forecasting model based on historical movement data to determine the optimal placement. The output of this process is information on the placement of transportation methods based on the forecast.

[0787] Step 6:

[0788] The terminal displays suggested transportation options received from the server to the user. This information includes details of the transportation method, fares, and route options. An interface is provided for the user to review and make a selection. The output is the transportation information presented to the user.

[0789] Step 7:

[0790] The user makes a selection based on the provided information. They then perform an action to notify the server that arrangements for the selected mode of transport are complete. This completes the system-wide operation. The output is information about the final decided mode of transport.

[0791] (Application Example 2)

[0792] 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".

[0793] Traditional transportation selection is based solely on the user's geographical location and destination, making it difficult to provide services tailored to the individual user's emotional state. This has resulted in situations where, even when users are stressed or experiencing specific emotional states, the appropriate travel experience cannot be provided. Therefore, there is a need to recognize each user's emotional state and select the most suitable mode of transportation accordingly, thereby providing a more personalized travel experience.

[0794] 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.

[0795] In this invention, the server includes information analysis means, learning means, demand forecasting means, and sentiment analysis means. This makes it possible to optimize the means of transportation by taking into account the user's geographical location and destination, as well as their emotional state at that time.

[0796] "Information analysis means" refers to a function that analyzes data such as the user's geographical location, destination, and past usage history in order to select the most suitable means of transportation.

[0797] A "learning tool" is a function that takes into account the user's preferences and past choices to inform the selection of the means of transportation.

[0798] A "demand forecasting tool" is a function that predicts the demand for transportation based on past usage patterns of modes of transport.

[0799] A "controlling mechanism" is a function that instructs the user on how the selected means of transport should operate.

[0800] "Emotional analysis tools" are functions that recognize the user's emotional state and adjust the selection of transportation methods based on that.

[0801] In the embodiments of this invention, the system is implemented using three main components: a user, a terminal, and a server.

[0802] ■System Configuration

[0803] Users utilize smartphones or smart glasses equipped with cameras and microphones. These devices capture the user's facial expressions and voice, and transmit them to an emotion analysis system. This system uses software called an emotion engine to analyze the user's emotional state.

[0804] ■Data Processing

[0805] The server processes data as an information analysis tool. It comprehensively analyzes the user's geographical location, destination, past usage history, and emotional state to select the optimal mode of transport and route. In this process, a generative AI model is used to enhance route suggestions based on the user's emotional state. The selected mode of transport and route information is transmitted to the user's terminal via an instruction device.

[0806] For example, if a user is in a highly stressed state, the emotion analysis system will detect this state, and the server will prioritize selecting a quiet and calming mode of transport. Relaxing music will be played during transport.

[0807] By providing the system with a prompt such as, "Please suggest the optimal travel route for this user to relax. The user is feeling stressed," the system's emotion analysis and information analysis capabilities can work together to provide the most suitable service.

[0808] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0809] Step 1:

[0810] The user uses a smartphone or smart glasses to input their ride request into a terminal application. During this process, the terminal captures information such as geographical location, destination, and preferred vehicle type, as well as the user's facial expressions and voice. The terminal then transmits the captured data to a sentiment analysis system.

[0811] Step 2:

[0812] The device uses an emotion engine to analyze the user's emotional state using captured facial expressions and voice data as input. Specifically, it recognizes the user's emotional state, such as stress, anxiety, and joy, from this data and outputs the result. The emotional state data is sent to a server.

[0813] Step 3:

[0814] The server receives geographical location, destination, user's past usage history, and emotional state as input for information analysis. This data is integrated, and a generative AI model is used to calculate and output the optimal mode of transport and route. During this process, the user's emotional state is taken into consideration when selecting the mode of transport.

[0815] Step 4:

[0816] The server uses demand forecasting tools to predict the demand for transportation methods across the entire region. Using past travel patterns as input, it determines and outputs the optimal allocation of transportation methods that are appropriate to the current situation.

[0817] Step 5:

[0818] The server transmits the selected mode of transport and route information to the user's terminal via a command system. During transport, suggestions are made to provide the user with a more comfortable travel experience (e.g., playing relaxing music).

[0819] Step 6:

[0820] Users review information about the received transportation method and experience the provided service. If necessary, they can submit feedback via their device, and data is collected to help improve future use.

[0821] Through the above processing flow, users can enjoy an optimal travel experience tailored to their emotional state.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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."

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] The following is further disclosed regarding the embodiments described above.

[0844] (Claim 1)

[0845] A data analysis tool for selecting the optimal mode of transport based on the geographical location and destination of the user of the mode of transport,

[0846] A learning means for learning the user's preferences and reflecting them in the selection of the means of transport,

[0847] A demand forecasting means for optimizing the arrangement of the aforementioned transport means,

[0848] An instruction means for providing instructions to the transport means,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, characterized in that the data analysis means utilizes a database that includes past usage history.

[0852] (Claim 3)

[0853] The system according to claim 1, wherein the demand forecasting means makes a forecast based on past usage patterns of means of transport.

[0854] "Example 1"

[0855] (Claim 1)

[0856] An information analysis tool for selecting the optimal mode of transport based on the geographical location and destination of the user of the mode of transport,

[0857] A learning device for learning the user's preferences and reflecting them in the selection of the means of transport,

[0858] A demand prediction device for optimizing the arrangement of the transport means,

[0859] A communication device for selecting a means of transport and providing optimized route information based on information received from the user,

[0860] An instruction device for providing detailed instructions to the transport means,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, characterized in that the information analysis means utilizes a recording device that includes past usage history.

[0864] (Claim 3)

[0865] The system according to claim 1, characterized in that the request prediction device makes predictions based on past usage patterns of means of transport.

[0866] "Application Example 1"

[0867] (Claim 1)

[0868] A data analysis tool for selecting the optimal mode of transport based on the geographical location and destination of the user of the mode of transport,

[0869] A learning means for learning the user's preferences and reflecting them in the selection of the means of transport,

[0870] A demand forecasting means for optimizing the arrangement of the aforementioned transport means,

[0871] An instruction means for providing instructions to the transport means,

[0872] A communication means for transmitting the operation information of the aforementioned means of transport to a user terminal in real time and requesting confirmation,

[0873] The terminal includes a display means that allows the user to visually monitor the operating status,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, characterized in that the data analysis means utilizes a database that includes past usage history.

[0877] (Claim 3)

[0878] The system according to claim 1, wherein the demand forecasting means makes a forecast based on past usage patterns of means of transport and time-based traffic data.

[0879] "Example 2 of combining an emotion engine"

[0880] (Claim 1)

[0881] An information processing means for selecting the optimal means of transport based on the geographical location and destination of the user of the means of transport,

[0882] A learning means for learning the user's preferences and emotional state and reflecting this in the selection of the means of transport,

[0883] A prediction means for optimizing the arrangement of the transport means,

[0884] An analysis means for analyzing the emotional state of the user from their voice and facial expressions,

[0885] A control means for providing instructions to the transport means,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, characterized in that the information processing means utilizes a set of information including past usage history.

[0889] (Claim 3)

[0890] The system according to claim 1, wherein the prediction means makes predictions based on past usage trends of means of transport.

[0891] "Application example 2 of combining emotional engines"

[0892] (Claim 1)

[0893] An information analysis tool for selecting the optimal mode of transport based on the geographical location and destination of the user of the mode of transport,

[0894] A learning means for learning the user's preferences and reflecting them in the selection of the means of transport,

[0895] A demand forecasting means for optimizing the arrangement of the aforementioned transport means,

[0896] An instruction means for providing instructions to the transport means,

[0897] An emotion analysis means for recognizing the emotional state of the user and adjusting the selection of the means of transport based on that,

[0898] A system that includes this.

[0899] (Claim 2)

[0900] The system according to claim 1, characterized in that the information analysis means utilizes a dataset that includes past usage history.

[0901] (Claim 3)

[0902] The system according to claim 1, wherein the demand forecasting means makes a forecast based on past usage patterns of means of transport. [Explanation of Symbols]

[0903] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A data analysis tool for selecting the optimal mode of transport based on the geographical location and destination of the user of the mode of transport, A learning means for learning the user's preferences and reflecting them in the selection of the means of transport, A demand forecasting means for optimizing the arrangement of the aforementioned transport means, An instruction means for providing instructions to the transport means, A communication means for transmitting the operation information of the aforementioned means of transport to a user terminal in real time and requesting confirmation, The terminal includes a display means that allows the user to visually monitor the operating status, A system that includes this.

2. The system according to claim 1, characterized in that the data analysis means utilizes a database that includes past usage history.

3. The system according to claim 1, characterized in that the demand forecasting means makes a forecast based on past usage patterns of means of transport and time-based traffic data.

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