system

Abstract

We provide the system. [Solution] A means of collecting data in real time from multiple sources via a communication network, A means for analyzing the data to generate the optimal operating route and schedule for logistics vehicles, A means for distributing the generated route and schedule to the terminals of the logistics vehicles, The terminal monitors various conditions of the logistics vehicle and transmits additional data for use in the aforementioned analysis. 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] There is a need to solve problems such as a shortage of drivers, low operating efficiency, and high environmental load in the logistics industry, and to realize a sustainable and efficient logistics network. Also, in conventional systems, it is difficult to effectively implement real-time situation changes and optimization of operating routes, and resulting logistics delays and cost increases have become problems.

Means for Solving the Problems

[0005] This invention provides a system that generates optimal routes and schedules for logistics vehicles by collecting data in real time from multiple sources via a communication network and analyzing it. The generated routes and schedules are delivered to terminals in the logistics vehicles, which can monitor various statuses of the vehicles and transmit additional data for further analysis. This system promotes efficient operation, resulting in cost reduction and a reduction in environmental impact.

[0006] A "communication network" is a collection of infrastructure and protocols that various devices and systems use to exchange data.

[0007] "Information source" refers to the source of data or the system or device that provides the data.

[0008] "Collecting data in real time" means the process of recording and acquiring information as soon as it is generated.

[0009] "Analysis" refers to the process of analyzing collected data to derive insights and conclusions aligned with a specific purpose.

[0010] A "logistics vehicle" refers to a vehicle used to transport goods or products.

[0011] A "route" refers to the path or route that a logistics vehicle takes when traveling to its destination.

[0012] A "schedule" is a plan or plan that includes the start and end times for a specific task or activity.

[0013] A "terminal" refers to a device used to display data and for users to interact with the system.

[0014] "Monitoring" refers to the process of continuously observing and recording the status of a device or system.

[0015] The "additional data for analysis" refers to information added to existing data that is used to further improve the optimization process.

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 a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

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

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

[0019] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, a 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, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

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

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

[0024] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0037] The smart logistics system of this invention utilizes a high-speed communication network to achieve efficient and environmentally friendly operation of logistics vehicles. The main processing flow and functions are described below.

[0038] server

[0039] The server first receives data acquired in real time from multiple sources. This includes road traffic information, logistics vehicle location information, and weather conditions. The received data is stored in the system's database and analyzed using generating AI. Through this analysis, the optimal route and schedule are generated. The generated route plan is quickly distributed to the terminals of each logistics vehicle.

[0040] terminal

[0041] The terminal notifies the driver of the route plan received from the server. Specifically, it displays the optimal route, departure time, and rest stops along the way. The terminal also monitors fuel levels and vehicle health using sensors installed in the vehicle and sends the necessary data to the server. In this way, the operational status is constantly monitored in real time, and optimization is performed.

[0042] User

[0043] Users can view optimized routes and schedules through their terminals. During their journey, they can use their terminals to follow instructions and adjust their plans as needed. For example, they can receive real-time suggestions for avoiding traffic congestion or making changes due to weather. After their journey, they are expected to provide feedback on the actual results to help improve the system's accuracy.

[0044] Through these processes, various challenges in the logistics industry are resolved, enabling highly efficient and low-cost operations. For example, if a logistics vehicle is traveling on a road with poor visibility due to rain, the server suggests an alternative route and notifies the driver via a terminal. In this way, the system supports safe and efficient logistics operations.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] The server collects traffic, weather, and location information in real time from multiple data sources. This information is gathered at high speed via a communication network and stored in a secure database.

[0048] Step 2:

[0049] The server analyzes the collected data using AI to calculate the optimal route for logistics vehicles. This analysis includes historical trend analysis and real-time situation assessment, performing optimization according to specific conditions and constraints.

[0050] Step 3:

[0051] The server creates the optimal operating schedule and route based on the analysis results and transmits this information to the terminals of the logistics vehicles. The plan is then delivered in real time using the communication network.

[0052] Step 4:

[0053] The terminal receives the route and schedule transmitted from the server and presents them to the driver. It displays visual route guidance and provides voice guidance as needed.

[0054] Step 5:

[0055] The terminal continuously monitors data from sensors installed in the vehicle, checking fuel levels and engine status. This data is transmitted to a server and used for further operational optimization.

[0056] Step 6:

[0057] The user checks the route plan displayed on the terminal and begins the operation according to that plan. If the situation changes during the operation, adjustments are made in real time according to the instructions on the terminal.

[0058] Step 7:

[0059] After completing a run, users provide feedback via their terminals, reporting actual run results and issues to the server. This feedback will be used to improve the system and enhance its accuracy in the future.

[0060] (Example 1)

[0061] 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."

[0062] In the face of constantly changing road and weather conditions, the safe and efficient operation of logistics vehicles is essential. However, conventional systems face challenges in providing optimal routes due to delays in data collection and analysis. Furthermore, there is a need to accurately grasp the status of logistics vehicles in operation and provide real-time feedback as needed.

[0063] 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.

[0064] In this invention, the server includes means for acquiring data in real time from multiple information sources via a communication medium, means for analyzing the data to generate an optimal route and time plan for a logistics vehicle, and means for transmitting the generated route and time plan to an information terminal of the logistics vehicle. This enables optimal real-time operation management of the logistics vehicle.

[0065] "Communication medium" refers to technical means such as networks used to send and receive data.

[0066] "Information sources" refer to organizations or devices that provide data such as traffic information, location information, and weather information.

[0067] "Real-time" refers to a state in which information is acquired, processed, and distributed almost instantly.

[0068] "Data" refers to the information necessary to optimize the operation of logistics vehicles.

[0069] "Analysis" refers to the process of analyzing acquired data and extracting useful information.

[0070] "Logistical transport vehicles" refer to vehicles used for transporting goods.

[0071] "Route" refers to the path that a moving object should follow when traveling to its destination.

[0072] "Time planning" refers to the allocation and schedule of time from the start to the end of an operation.

[0073] An "information terminal" refers to a device installed on a mobile logistics vehicle for receiving operational plans and monitoring its status.

[0074] A "detection device" refers to a sensor used to measure the state of the environment.

[0075] An "imaging device" refers to a camera used to acquire visual information.

[0076] "Road conditions" refers to the road surface and traffic conditions in the direction of travel.

[0077] "Weather information" refers to data related to weather conditions.

[0078] "Deviation" refers to going off-plan or deviating from a set route or plan.

[0079] "Notification" refers to the act of informing a user of specific information.

[0080] This invention provides a system that utilizes a high-speed and stable communication medium to receive and analyze diverse data in real time, in order to optimize the operation of mobile logistics vehicles. This system consists of a server, terminals, and users, each playing a specific role.

[0081] server

[0082] The server collects various data in real time via communication media, including road traffic data, location information of moving goods, and weather information. Based on this data stored in the system's database, analysis is performed using a generative AI model. The AI ​​model is trained using machine learning frameworks such as TENSORFLOW® and PyTorch, and automatically generates optimal routes and time plans.

[0083] For example, if a logistics vehicle determines that it cannot be safely transported via its usual route due to heavy rain, the server will immediately generate and propose an alternative route.

[0084] Examples of prompts to input into a generative AI model:

[0085] "Use traffic information, location information, and weather information to generate the optimal logistics route and propose a schedule that considers safety and efficiency."

[0086] terminal

[0087] The terminal transmits the optimized route plan received from the server to the driver. Furthermore, it has the function of constantly monitoring data such as fuel level and vehicle status via sensors installed in the vehicle and transmitting this data to the server. This enables immediate responses and adjustments to the plan in response to the situation during operation.

[0088] User

[0089] The user (driver) operates based on information provided by the terminal. By operating according to real-time route information, efficient and safe operation is possible. In addition, performance data can be fed back after the operation is completed, contributing to system improvements. This will further improve the accuracy of routes in subsequent operations.

[0090] In this way, the present invention provides an effective means for safely and efficiently operating logistics vehicles.

[0091] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0092] Step 1:

[0093] The server receives data in real time from multiple sources using communication media. It takes traffic information, location information, and weather information as input and stores this data in a database. API calls are used to receive data, which is updated every minute. For example, based on input data obtained from a traffic information API, the server adds the current congestion status of major roads to the database.

[0094] Step 2:

[0095] The server performs analysis using an AI model based on data stored in the database. It inputs acquired road traffic data, location data, and weather data into the AI ​​model to generate the optimal route and travel schedule. Data processing includes data cleaning and preprocessing using Python, and the output is the calculated route and schedule. For example, the AI ​​model quantifies the impact of weather and predicts the travel time of alternative routes, taking this into account.

[0096] Step 3:

[0097] The server sends the generated optimal route and schedule to the terminal. Here, the generated plan is serialized in JSON format and transferred over the network to the information terminal of the logistics vehicle. For example, the server automatically sends the new operating schedule to all logistics vehicles for the day at 5 a.m.

[0098] Step 4:

[0099] The terminal notifies the driver of the route plan received from the server. It outputs the received route and schedule data by displaying it on the terminal's screen. Furthermore, it monitors fuel levels and engine health through its built-in sensors and transmits this data to the server. For example, the terminal displays an alert to the driver when the fuel level falls below a certain level.

[0100] Step 5:

[0101] The user (driver) operates based on information provided on the terminal. Road conditions and route information obtained from the terminal are used as input for operation, and upon completion of the operation, the results are input as feedback to the terminal. This is recorded as output and stored on the server as reference data for the next operation. For example, the driver can avoid congestion by selecting a suggested alternative route based on real-time road information.

[0102] (Application Example 1)

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

[0104] In the logistics industry, improving operational efficiency and ensuring sustainability are crucial challenges. In particular, route optimization that responds to real-time environmental changes is required, but conventional systems struggle to achieve this effectively. This often leads to problems such as transportation delays, increased costs, and greater environmental impact.

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

[0106] In this invention, the server includes means for collecting data in real time from multiple information sources via a communication network, means for analyzing the data to generate an optimal route and plan for logistics equipment, and means for distributing the generated route and plan to terminals of the logistics equipment. This enables real-time monitoring of operational status and dynamic route adjustment.

[0107] A "communication network" is an infrastructure for exchanging data between multiple devices that are geographically separated.

[0108] "Information source" refers to the recipient or device used to obtain the necessary data.

[0109] "Real-time" is a concept that indicates that data and information are updated instantaneously.

[0110] "Means of analyzing data" refer to devices and technologies that extract useful information based on acquired data and perform processing to support decision-making.

[0111] "Logistics equipment" refers to vehicles and machinery used to transport goods and products.

[0112] "Operating route" refers to the optimal route for traveling between designated points.

[0113] "Planning" refers to designing detailed procedures for operations or activities to be carried out in order to achieve a specific objective.

[0114] A "terminal" is a device used by users to input, display, and manage digital information.

[0115] "Environmental information" refers to data about the surrounding conditions when logistics equipment is in motion.

[0116] "Dynamic route adjustment" is a technique that makes immediate changes to optimize the route based on real-time data.

[0117] In an embodiment of this invention, a server plays a central role. The server collects real-time data from multiple sources, such as traffic information, weather information, and location information of logistics equipment, via a communication network. The collected data is stored in the system's database and analyzed using a generative AI model. This analysis generates the optimal route and plan. The generated route and plan are then quickly distributed to the terminals of each logistics device.

[0118] The terminal notifies the user of the route and plan received from the server via a display device. This display device is often implemented on a smartphone or tablet. The terminal also has the function of sending data on fuel status and equipment health obtained from sensors installed on the logistics equipment to the server. This allows for constant monitoring of the logistics equipment's real-time operation status and enables dynamic route adjustments as needed.

[0119] For example, if sudden weather changes or traffic congestion are predicted along a designated route, the server immediately generates a new, optimal route and notifies the user of the revised plan via their terminal. This makes it possible to maximize operational efficiency while minimizing environmental impact.

[0120] An example of a prompt for a generating AI model is: "Consider the weather conditions for the next 24 hours and generate the optimal route between the following points: origin A, intermediate point B, destination C." This prompt allows the AI ​​to quickly provide a suitable route.

[0121] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0122] Step 1:

[0123] The server receives real-time data such as traffic information, weather data, and location information of logistics equipment as input data collected from multiple sources via a communication network. This information is stored in a database. Protocols such as APIs and WebSockets are used for data collection, and Python and SQL are used for data organization.

[0124] Step 2:

[0125] The server inputs data stored in the database into an AI model and performs data analysis to generate the optimal route and schedule as output. A deep learning model using TensorFlow is employed for the analysis, making predictions that take into account traffic flow and weather patterns. In this process, the AI ​​analyzes real-time data and calculates the most efficient route.

[0126] Step 3:

[0127] The generated routes and schedules are delivered as output data from the server to the logistics equipment terminals. The terminals receive this information and notify the user of the route and schedule in real time via a display device. The user view is updated via a smartphone app built with React Native.

[0128] Step 4:

[0129] The terminal's sensors monitor the fuel status and health condition of the logistics equipment and transmit this information as input data to the server. This allows the server to constantly monitor the equipment's operating status, perform necessary recalculations, and continuously provide optimal route suggestions.

[0130] Step 5:

[0131] Users can monitor the situation through the terminal's display during operation and adjust the plan as needed. Specifically, when dynamically changing routes in consideration of traffic congestion or weather changes, the system provides instructions based on the alternative routes displayed to the user. This enables efficient and safe operation.

[0132] 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.

[0133] The system of the present invention, in order to optimize the operation of logistics vehicles, collects diverse information via a communication network and generates operating routes and schedules based on that information, and also incorporates an emotion engine that recognizes user emotions. The functions of each component and their interactions are described in detail below.

[0134] server

[0135] The server collects real-time information from a variety of high-speed data sources via a communication network. This includes traffic conditions, weather information, and vehicle status. The collected data is stored in a database and analyzed using generative AI and an emotion engine. The emotion engine analyzes the user's state and detects specific emotions. For example, it can process data obtained from voice input and camera footage to assess the user's stress level. Taking this information into consideration, the server generates the optimal route and schedule, making adjustments based on the emotional state.

[0136] terminal

[0137] The terminal receives information transmitted from the server and presents it to the driver. The route and schedule are displayed visually on the user interface and guided by voice. The tone and frequency of the voice guidance are adjusted based on the user's emotional state. For example, if the user is stressed, the tone of the voice guidance is softened and the frequency is reduced to help the driver relax.

[0138] User

[0139] Users can check the route and schedule via their device and operate the vehicle according to the instructions. Furthermore, by providing feedback on their emotional state through the device, the system can perform even more accurate analysis. For example, after a user safely completes an operation, they can provide feedback on their emotional changes, which can then be used as data for future emotional analysis.

[0140] This invention not only improves the efficiency of conventional logistics operations but also reduces the psychological burden on drivers, enabling highly efficient and safe operations. For example, if sudden traffic congestion occurs during operation, the server calculates alternative routes in real time, and if the emotion engine detects an increase in the user's stress level, it quickly adjusts the guidance to reduce the burden on the user. In this way, the system comprehensively supports logistics operations.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] The server collects real-time data, including traffic information, weather data, location information, and vehicle operating status, via the communication network. This includes information from sensors and cameras installed on roads and GPS devices installed in vehicles.

[0144] Step 2:

[0145] The device collects audio and video data from the driver using microphones and cameras inside the vehicle. This data is used to analyze the user's emotional state.

[0146] Step 3:

[0147] The server analyzes the collected operational and emotional data. The generative AI optimizes the operational routes, and the emotional engine determines the user's emotional state. For example, it detects signs of stress or fatigue.

[0148] Step 4:

[0149] The server creates an optimized route and schedule based on the analysis results and delivers it to the terminal, taking into account the user's emotional state. If the user is emotionally unstable, the nearest rest stop may be added to the route.

[0150] Step 5:

[0151] The terminal displays the received route and schedule to the driver visually and audibly. The voice guidance is adjusted in tone and tempo according to the driver's emotional state.

[0152] Step 6:

[0153] The user begins operation according to the instructions displayed on the terminal and safely adapts to the operating conditions according to the system's instructions. For example, the route may be changed depending on the degree of road congestion.

[0154] Step 7:

[0155] During operation, the terminal continues to monitor vehicle status and driver sentiment data, and sends feedback to the server as needed. The server uses this information to revise the operation plan in real time and redistribute it to the user.

[0156] Step 8:

[0157] After the operation ends, users provide feedback on the entire operation via their devices, sending data to the server that contributes to improving the accuracy of emotion recognition. This feedback will be used to improve future operation plans.

[0158] (Example 2)

[0159] 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".

[0160] Conventional logistics operation systems can optimize routes based on real-time traffic and weather information, but they cannot take into account the emotional state of drivers, such as their psychological burden. As a result, they have problems ensuring sufficient safety and efficiency in operations. Furthermore, they have problems effectively utilizing information from various sensors and not being able to cope with dynamically changing operating environments.

[0161] 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.

[0162] In this invention, the server includes means for collecting current data from various information sources via a communication infrastructure, means for analyzing the data and generating an optimal travel route and plan for the transport vehicle, and means for an emotion recognition device that evaluates the user's emotional state based on the collected data and adjusts the route and guidance based on the evaluation results. This improves operational efficiency while simultaneously enabling flexible support tailored to the driver's emotional state.

[0163] "Communication infrastructure" refers to the entire basic structure for sending and receiving information, including networks and servers.

[0164] "Diverse information sources" refers to multiple data providers that offer information such as traffic conditions, weather data, and vehicle sensor data.

[0165] "Data analysis" refers to the computational process used to derive the optimal travel route and schedule based on collected information.

[0166] A "travel route" refers to the recommended path for a logistics vehicle to travel to its destination.

[0167] "Plan" refers to a schedule of physical movement, including the date, time, sequence, and method.

[0168] "Device" refers to a terminal device that has functions such as displaying information or providing voice guidance.

[0169] An "emotion recognition device" refers to a system that analyzes a user's emotions from audio and video and evaluates their emotional state.

[0170] A "warning" refers to a message that detects a deviation from a set travel path and, if necessary, provides a warning.

[0171] A "recalculated route" refers to a newly generated alternative route to accommodate unexpected events or changes.

[0172] This invention is a system aimed at improving the efficiency of logistics vehicle operations and providing flexible support tailored to the driver's emotional state. The system mainly consists of a server, terminals, and user interaction.

[0173] server

[0174] The server utilizes communication infrastructure to collect real-time data from various sources. This data includes traffic information, weather data, and vehicle status information. The server stores this data in a database and uses a generative AI model to calculate the optimal travel route and schedule. Simultaneously, it analyzes the user's voice and facial expression data using an emotion recognition device to evaluate their emotional state. The server integrates these results and generates prompt messages. For example, it might generate a prompt message such as, "Current traffic conditions are congested. We suggest an alternative route to reach your destination safely and quickly. Also, since your emotions indicate tension, we prioritize relaxing voice guidance."

[0175] terminal

[0176] The terminal is responsible for presenting the driver with route information and emotion-based instructions received from the server. The terminal is equipped with a display that visually shows a map and a speaker for voice guidance. To respond to sudden changes in driving conditions, the terminal can update information in real time and flexibly adjust the guidance.

[0177] User

[0178] The driver, as the user, operates the vehicle according to the route and schedule provided through the terminal. Furthermore, by providing feedback on their emotional state using the terminal, the server can utilize this data for future analysis. For example, by providing feedback such as "I feel more relaxed after completing the trip," it is possible to improve the accuracy of the emotional analysis.

[0179] In this way, the system provides optimal operational support based on real-time information and user sentiment, improving operational efficiency and safety.

[0180] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0181] Step 1:

[0182] The server collects data from various sources, such as traffic conditions, weather information, and vehicle status, in real time via the communication infrastructure. It receives data from traffic sensors, weather APIs, and vehicle sensors as input and stores it in a database. This provides the foundational data needed for subsequent analysis steps.

[0183] Step 2:

[0184] The server inputs the collected data into a generating AI model to calculate the optimal travel route and schedule. This data processing takes into account traffic congestion and weather conditions, while also referencing historical travel data. The output is the recommended travel route and schedule, which is used to generate prompt messages.

[0185] Step 3:

[0186] The server inputs voice data and camera footage into an emotion recognition device to evaluate the user's emotional state. Specifically, it analyzes changes in voice tone and facial expressions to determine the degree of stress and tension. This analysis result is used to adjust guidance according to the user's emotions.

[0187] Step 4:

[0188] The server integrates the output of the generating AI model with the emotion recognition results to generate a prompt message. This prompt message might include something like, "Traffic is currently congested. We suggest an alternative route. Also, because the user's emotion indicates tension, we prioritize relaxing voice guidance." This integration process clarifies the instructions given to the driver.

[0189] Step 5:

[0190] The terminal receives prompt messages and route information sent from the server and presents them to the driver. It receives data from the server as input and outputs it visually and audibly through digital maps and voice guidance. This allows the driver to proceed with the actual operation. Furthermore, the tone and frequency of voice guidance are adjusted according to the driver's emotional state.

[0191] Step 6:

[0192] The user operates the vehicle based on information obtained from their device and provides feedback to the server after the operation is complete. As input, the device records changes in emotions during operation and sends this data to the server. As output, the user's feedback is used for future analysis, enabling more accurate operational support.

[0193] (Application Example 2)

[0194] 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".

[0195] In logistics, there is a need to improve operational efficiency while reducing driver stress and burden. However, current systems cannot perform real-time sentiment analysis, which can easily increase drivers' mental burden. Furthermore, it is difficult for logistics managers to immediately grasp operational status and drivers' emotional states and provide appropriate support. These challenges need to be addressed.

[0196] 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.

[0197] In this invention, the server includes means for collecting data in real time from multiple information sources via a communication network, means for analyzing the data to generate an optimal route and schedule, and means for analyzing the user's emotions and adjusting the tone and frequency of voice output. This makes it possible to optimize the route, reduce the psychological burden on the driver, and notify the logistics manager of the operational information and emotional state via a visualization device.

[0198] A "communication network" is an infrastructure for sending and receiving data in real time between different devices.

[0199] A "data source" refers to a source that provides various types of data, including traffic conditions and weather information.

[0200] A "route" is the optimal path that a logistics vehicle should take to reach its destination.

[0201] A "schedule" refers to a time-based plan for the operation of logistics vehicles.

[0202] A "display device" is a device that visually presents the route and schedule to the driver.

[0203] "Monitoring" refers to the continuous monitoring of the condition of logistics vehicles.

[0204] "Additional data" refers to further information regarding the operation of logistics vehicles, which is used for analysis.

[0205] "Emotional analysis" is the process of determining a user's mental state from data.

[0206] "Voice output" refers to a system for conveying operational information and announcements through sound.

[0207] A "visualization device" is a device that allows logistics managers to visually check operational information and emotional states.

[0208] The system that realizes this invention first involves a server collecting data in real time from various sources via a communication network. This includes traffic conditions, weather information, and vehicle status. The collected data is stored in a database and analyzed using a generative AI model and an emotion engine. Based on the collected data, the server generates the optimal route and schedule and delivers it to the display devices of the logistics vehicles.

[0209] The display device visually shows the driver the route and schedule transmitted from the server. In addition, the display device also provides guidance through voice output. The tone and frequency of the voice output are adjusted based on the results of an emotional engine's analysis of the driver's emotional state. For example, if the engine analyzes that the driver is in a state of tension, the tone of the voice guidance will be softened and the frequency of navigation will be reduced to promote relaxation.

[0210] Furthermore, logistics managers can use visualization devices to check operational information and drivers' emotional states in real time. This allows for smoother troubleshooting and online support in emergencies. For example, if a driver encounters traffic congestion due to sudden rain and the emotional system detects a high-stress state, this information is immediately shared with the logistics manager, allowing for appropriate advice to be provided.

[0211] The AI ​​generation model can generate prompt messages such as, "Take a deep breath and exhale slowly for 5 seconds. You will be guided to an alternative route, so please don't worry," making it possible to convey appropriate instructions to drivers and logistics managers.

[0212] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0213] Step 1:

[0214] The server collects data in real time from multiple sources, such as traffic conditions, weather information, and vehicle status, via a communication network. This data is obtained from each source as APIs or sensor data and stored in a database within the system. The input is real-time environmental data, and the output is data stored in a structured database.

[0215] Step 2:

[0216] The server analyzes the collected data and uses a generated AI model to calculate the most efficient routes and schedules for logistics vehicles. It receives structured environmental data as input, optimizes routes and schedules based on the AI ​​model, and outputs this in digital format. Specific operations include traffic flow prediction and execution of time optimization algorithms.

[0217] Step 3:

[0218] The terminal receives the route and schedule transmitted from the server and outputs them as visual and audio guidance to the driver's display device. The input is route planning data from the server, and the output is a route map and voice guidance displayed on the user interface. The terminal analyzes navigation information in real time and immediately provides updated information if the route changes.

[0219] Step 4:

[0220] The server uses an emotion engine to analyze the driver's voice and physical data, which are inputs from the terminal, and evaluates the driver's emotional state. The input is voice analysis data, and the output is information about the driver's stress level and emotional state. Based on the emotions, the server generates appropriate voice guidance to reduce stress.

[0221] Step 5:

[0222] Users utilize operational information viewed through their terminals in actual operations, and also input feedback on their own emotional state into the terminal. The input consists of the driver's operational experience and emotional feedback, while the output is data that helps improve the system for future use. This feedback contributes to continuously improving the accuracy of emotional analysis.

[0223] Step 6:

[0224] The logistics manager's visualization device receives operational status and sentiment information distributed from the server and displays it in the administrator view. Input is operational and sentiment data from the server, and output is a visualized management screen. Its specific functions include real-time status monitoring on the dashboard and emergency response support.

[0225] 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.

[0226] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0227] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0228] [Second Embodiment]

[0229] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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).

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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".

[0241] The smart logistics system of this invention utilizes a high-speed communication network to achieve efficient and environmentally friendly operation of logistics vehicles. The main processing flow and functions are described below.

[0242] server

[0243] The server first receives data acquired in real time from multiple sources. This includes road traffic information, logistics vehicle location information, and weather conditions. The received data is stored in the system's database and analyzed using generating AI. Through this analysis, the optimal route and schedule are generated. The generated route plan is quickly distributed to the terminals of each logistics vehicle.

[0244] terminal

[0245] The terminal notifies the driver of the route plan received from the server. Specifically, it displays the optimal route, departure time, and rest stops along the way. The terminal also monitors fuel levels and vehicle health using sensors installed in the vehicle and sends the necessary data to the server. In this way, the operational status is constantly monitored in real time, and optimization is performed.

[0246] User

[0247] Users can view optimized routes and schedules through their terminals. During their journey, they can use their terminals to follow instructions and adjust their plans as needed. For example, they can receive real-time suggestions for avoiding traffic congestion or making changes due to weather. After their journey, they are expected to provide feedback on the actual results to help improve the system's accuracy.

[0248] Through these processes, various challenges in the logistics industry are resolved, enabling highly efficient and low-cost operations. For example, if a logistics vehicle is traveling on a road with poor visibility due to rain, the server suggests an alternative route and notifies the driver via a terminal. In this way, the system supports safe and efficient logistics operations.

[0249] The following describes the processing flow.

[0250] Step 1:

[0251] The server collects traffic, weather, and location information in real time from multiple data sources. This information is gathered at high speed via a communication network and stored in a secure database.

[0252] Step 2:

[0253] The server analyzes the collected data using AI to calculate the optimal route for logistics vehicles. This analysis includes historical trend analysis and real-time situation assessment, performing optimization according to specific conditions and constraints.

[0254] Step 3:

[0255] The server creates the optimal operating schedule and route based on the analysis results and transmits this information to the terminals of the logistics vehicles. The plan is then delivered in real time using the communication network.

[0256] Step 4:

[0257] The terminal receives the route and schedule transmitted from the server and presents them to the driver. It displays visual route guidance and provides voice guidance as needed.

[0258] Step 5:

[0259] The terminal continuously monitors data from sensors installed in the vehicle, checking fuel levels and engine status. This data is transmitted to a server and used for further operational optimization.

[0260] Step 6:

[0261] The user checks the route plan displayed on the terminal and begins the operation according to that plan. If the situation changes during the operation, adjustments are made in real time according to the instructions on the terminal.

[0262] Step 7:

[0263] After completing a run, users provide feedback via their terminals, reporting actual run results and issues to the server. This feedback will be used to improve the system and enhance its accuracy in the future.

[0264] (Example 1)

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

[0266] In the face of constantly changing road and weather conditions, the safe and efficient operation of logistics vehicles is essential. However, conventional systems face challenges in providing optimal routes due to delays in data collection and analysis. Furthermore, there is a need to accurately grasp the status of logistics vehicles in operation and provide real-time feedback as needed.

[0267] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0268] In this invention, the server includes means for acquiring data in real time from multiple information sources via a communication medium, means for analyzing the data to generate an optimal route and time plan for a logistics vehicle, and means for transmitting the generated route and time plan to an information terminal of the logistics vehicle. This enables optimal real-time operation management of the logistics vehicle.

[0269] "Communication medium" refers to technical means such as networks used to send and receive data.

[0270] "Information sources" refer to organizations or devices that provide data such as traffic information, location information, and weather information.

[0271] "Real-time" refers to a state in which information is acquired, processed, and distributed almost instantly.

[0272] "Data" refers to the information necessary to optimize the operation of a logistics moving body.

[0273] "Analysis" refers to the process of analyzing the acquired data and extracting useful information.

[0274] "Logistics moving body" refers to a vehicle used for transporting goods.

[0275] "Route" refers to the path that a logistics moving body should follow when heading towards a destination.

[0276] "Time plan" refers to the allocation and schedule of time from the start to the end of operation.

[0277] "Information terminal" refers to a device installed on a logistics moving body for receiving operation plans and monitoring the status.

[0278] "Detection device" refers to a sensor for measuring the state of the environment.

[0279] "Imaging device" refers to a camera for acquiring visual information.

[0280] "Road condition" refers to the road surface and traffic conditions in the direction of travel.

[0281] "Weather information" refers to data related to the weather.

[0282] "Deviation" refers to deviating from the set route or plan.

[0283] "Notification" refers to the act of informing a user of specific information.

[0284] In the present invention, in order to optimize the operation of a logistics moving body, a system is provided that utilizes a high-speed and stable communication medium to receive and analyze various data in real time. This system consists of a server, a terminal, and a user, each playing a specific role.

[0285] Server

[0286] The server collects various data including road traffic situation data, the location information of logistics moving bodies, and further weather information in real-time via a communication medium. Based on the data stored in the in-system database, analysis is performed using a generated AI model. The AI model is trained using machine learning frameworks such as TensorFlow and PyTorch and automatically generates an optimal route and time plan.

[0287] As a specific example, when it is determined that a logistics moving body cannot ensure safety on the normal route due to the influence of heavy rain, the server immediately generates and proposes an alternative route.

[0288] Example of a prompt sentence input to the generated AI model:

[0289] "Generate an optimal logistics route using traffic information, location information, and weather information, and propose a schedule considering safety and efficiency."

[0290] Terminal

[0291] The terminal conveys the optimized route plan received from the server to the driver. Furthermore, it also has the function of constantly monitoring data such as the remaining fuel amount and the vehicle state through sensors equipped on the vehicle and transmitting this to the server. This enables immediate response and adjustment of the plan according to the situation during operation.

[0292] User

[0293] The user (driver) conducts operations based on the information provided by the terminal. By operating according to the real-time provided route information, it becomes possible to achieve operations that balance efficiency and safety. Also, after the operation is completed, performance data can be fed back, contributing to the improvement of the system. This further improves the accuracy of the route in subsequent operations.

[0294] In this way, the present invention provides an effective means for safely and efficiently operating logistics vehicles.

[0295] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0296] Step 1:

[0297] The server receives data in real time from multiple sources using communication media. It takes traffic information, location information, and weather information as input and stores this data in a database. API calls are used to receive data, which is updated every minute. For example, based on input data obtained from a traffic information API, the server adds the current congestion status of major roads to the database.

[0298] Step 2:

[0299] The server performs analysis using an AI model based on data stored in the database. It inputs acquired road traffic data, location data, and weather data into the AI ​​model to generate the optimal route and travel schedule. Data processing includes data cleaning and preprocessing using Python, and the output is the calculated route and schedule. For example, the AI ​​model quantifies the impact of weather and predicts the travel time of alternative routes, taking this into account.

[0300] Step 3:

[0301] The server sends the generated optimal route and schedule to the terminal. Here, the generated plan is serialized in JSON format and transferred over the network to the information terminal of the logistics vehicle. For example, the server automatically sends the new operating schedule to all logistics vehicles for the day at 5 a.m.

[0302] Step 4:

[0303] The terminal notifies the driver of the operation plan received from the server. As input, it outputs by displaying the received route and schedule data on the display of the terminal. Furthermore, it monitors the remaining fuel amount and the health status of the engine through the installed sensors, and transmits this data to the server. The terminal displays an alert to the driver, for example, when the fuel level drops below a certain level.

[0304] Step 5:

[0305] The user (driver) operates based on the information provided on the terminal. As input, the driver utilizes the road conditions and route information obtained from the terminal for the operation, and when the operation is completed, inputs the performance as feedback to the terminal. This is recorded as output and stored in the server as reference data for the next operation. For example, the driver selects a proposed alternative route based on real-time road information to avoid congestion.

[0306] (Application Example 1)

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

[0308] We will use the following text for the remaining part of the translation: In the logistics industry, improving operation efficiency and ensuring sustainability are important issues. In particular, route optimization corresponding to real-time environmental changes is required, but it is difficult to effectively achieve these in conventional systems. As a result, there are problems such as transportation delays, cost increases, and increased environmental impact.

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

[0310] In this invention, the server includes means for collecting data in real time from multiple information sources via a communication network, means for analyzing the data to generate an optimal route and plan for logistics equipment, and means for distributing the generated route and plan to terminals of the logistics equipment. This enables real-time monitoring of operational status and dynamic route adjustment.

[0311] A "communication network" is an infrastructure for exchanging data between multiple devices that are geographically separated.

[0312] "Information source" refers to the recipient or device used to obtain the necessary data.

[0313] "Real-time" is a concept that indicates that data and information are updated instantaneously.

[0314] "Means of analyzing data" refer to devices and technologies that extract useful information based on acquired data and perform processing to support decision-making.

[0315] "Logistics equipment" refers to vehicles and machinery used to transport goods and products.

[0316] "Operating route" refers to the optimal route for traveling between designated points.

[0317] "Planning" refers to designing detailed procedures for operations or activities to be carried out in order to achieve a specific objective.

[0318] A "terminal" is a device used by users to input, display, and manage digital information.

[0319] "Environmental information" refers to data about the surrounding conditions when logistics equipment is in motion.

[0320] "Dynamic route adjustment" is a technique that makes immediate changes to optimize the route based on real-time data.

[0321] In an embodiment of this invention, a server plays a central role. The server collects real-time data from multiple sources, such as traffic information, weather information, and location information of logistics equipment, via a communication network. The collected data is stored in the system's database and analyzed using a generative AI model. This analysis generates the optimal route and plan. The generated route and plan are then quickly distributed to the terminals of each logistics device.

[0322] The terminal notifies the user of the route and plan received from the server via a display device. This display device is often implemented on a smartphone or tablet. The terminal also has the function of sending data on fuel status and equipment health obtained from sensors installed on the logistics equipment to the server. This allows for constant monitoring of the logistics equipment's real-time operation status and enables dynamic route adjustments as needed.

[0323] For example, if sudden weather changes or traffic congestion are predicted along a designated route, the server immediately generates a new, optimal route and notifies the user of the revised plan via their terminal. This makes it possible to maximize operational efficiency while minimizing environmental impact.

[0324] An example of a prompt for a generating AI model is: "Consider the weather conditions for the next 24 hours and generate the optimal route between the following points: origin A, intermediate point B, destination C." This prompt allows the AI ​​to quickly provide a suitable route.

[0325] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0326] Step 1:

[0327] The server receives real-time data such as traffic information, weather data, and location information of logistics equipment as input data collected from multiple sources via a communication network. This information is stored in a database. Protocols such as APIs and WebSockets are used for data collection, and Python and SQL are used for data organization.

[0328] Step 2:

[0329] The server inputs data stored in the database into an AI model and performs data analysis to generate the optimal route and schedule as output. A deep learning model using TensorFlow is employed for the analysis, making predictions that take into account traffic flow and weather patterns. In this process, the AI ​​analyzes real-time data and calculates the most efficient route.

[0330] Step 3:

[0331] The generated routes and schedules are delivered as output data from the server to the logistics equipment terminals. The terminals receive this information and notify the user of the route and schedule in real time via a display device. The user view is updated via a smartphone app built with React Native.

[0332] Step 4:

[0333] The terminal's sensors monitor the fuel status and health condition of the logistics equipment and transmit this information as input data to the server. This allows the server to constantly monitor the equipment's operating status, perform necessary recalculations, and continuously provide optimal route suggestions.

[0334] Step 5:

[0335] Users can monitor the situation through the terminal's display during operation and adjust the plan as needed. Specifically, when dynamically changing routes in consideration of traffic congestion or weather changes, the system provides instructions based on the alternative routes displayed to the user. This enables efficient and safe operation.

[0336] 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.

[0337] The system of the present invention, in order to optimize the operation of logistics vehicles, collects diverse information via a communication network and generates operating routes and schedules based on that information, and also incorporates an emotion engine that recognizes user emotions. The functions of each component and their interactions are described in detail below.

[0338] server

[0339] The server collects real-time information from a variety of high-speed data sources via a communication network. This includes traffic conditions, weather information, and vehicle status. The collected data is stored in a database and analyzed using generative AI and an emotion engine. The emotion engine analyzes the user's state and detects specific emotions. For example, it can process data obtained from voice input and camera footage to assess the user's stress level. Taking this information into consideration, the server generates the optimal route and schedule, making adjustments based on the emotional state.

[0340] terminal

[0341] The terminal receives information transmitted from the server and presents it to the driver. The route and schedule are displayed visually on the user interface and guided by voice. The tone and frequency of the voice guidance are adjusted based on the user's emotional state. For example, if the user is stressed, the tone of the voice guidance is softened and the frequency is reduced to help the driver relax.

[0342] User

[0343] Users can check the route and schedule via their device and operate the vehicle according to the instructions. Furthermore, by providing feedback on their emotional state through the device, the system can perform even more accurate analysis. For example, after a user safely completes an operation, they can provide feedback on their emotional changes, which can then be used as data for future emotional analysis.

[0344] This invention not only improves the efficiency of conventional logistics operations but also reduces the psychological burden on drivers, enabling highly efficient and safe operations. For example, if sudden traffic congestion occurs during operation, the server calculates alternative routes in real time, and if the emotion engine detects an increase in the user's stress level, it quickly adjusts the guidance to reduce the burden on the user. In this way, the system comprehensively supports logistics operations.

[0345] The following describes the processing flow.

[0346] Step 1:

[0347] The server collects real-time data, including traffic information, weather data, location information, and vehicle operating status, via the communication network. This includes information from sensors and cameras installed on roads and GPS devices installed in vehicles.

[0348] Step 2:

[0349] The device collects audio and video data from the driver using microphones and cameras inside the vehicle. This data is used to analyze the user's emotional state.

[0350] Step 3:

[0351] The server analyzes the collected operational and emotional data. The generative AI optimizes the operational routes, and the emotional engine determines the user's emotional state. For example, it detects signs of stress or fatigue.

[0352] Step 4:

[0353] The server creates an optimized route and schedule based on the analysis results and delivers it to the terminal, taking into account the user's emotional state. If the user is emotionally unstable, the nearest rest stop may be added to the route.

[0354] Step 5:

[0355] The terminal displays the received route and schedule to the driver visually and audibly. The voice guidance is adjusted in tone and tempo according to the driver's emotional state.

[0356] Step 6:

[0357] The user begins operation according to the instructions displayed on the terminal and safely adapts to the operating conditions according to the system's instructions. For example, the route may be changed depending on the degree of road congestion.

[0358] Step 7:

[0359] During operation, the terminal continues to monitor vehicle status and driver sentiment data, and sends feedback to the server as needed. The server uses this information to revise the operation plan in real time and redistribute it to the user.

[0360] Step 8:

[0361] After the operation ends, users provide feedback on the entire operation via their devices, sending data to the server that contributes to improving the accuracy of emotion recognition. This feedback will be used to improve future operation plans.

[0362] (Example 2)

[0363] 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".

[0364] Conventional logistics operation systems can optimize routes based on real-time traffic and weather information, but they cannot take into account the emotional state of drivers, such as their psychological burden. As a result, they have problems ensuring sufficient safety and efficiency in operations. Furthermore, they have problems effectively utilizing information from various sensors and not being able to cope with dynamically changing operating environments.

[0365] 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.

[0366] In this invention, the server includes means for collecting current data from various information sources via a communication infrastructure, means for analyzing the data and generating an optimal travel route and plan for the transport vehicle, and means for an emotion recognition device that evaluates the user's emotional state based on the collected data and adjusts the route and guidance based on the evaluation results. This improves operational efficiency while simultaneously enabling flexible support tailored to the driver's emotional state.

[0367] "Communication infrastructure" refers to the entire basic structure for sending and receiving information, including networks and servers.

[0368] "Diverse information sources" refers to multiple data providers that offer information such as traffic conditions, weather data, and vehicle sensor data.

[0369] "Data analysis" refers to the computational process used to derive the optimal travel route and schedule based on collected information.

[0370] A "travel route" refers to the recommended path for a logistics vehicle to travel to its destination.

[0371] "Plan" refers to a schedule of physical movement, including the date, time, sequence, and method.

[0372] "Device" refers to a terminal device that has functions such as displaying information or providing voice guidance.

[0373] An "emotion recognition device" refers to a system that analyzes a user's emotions from audio and video and evaluates their emotional state.

[0374] A "warning" refers to a message that detects a deviation from a set travel path and, if necessary, provides a warning.

[0375] A "recalculated route" refers to a newly generated alternative route to accommodate unexpected events or changes.

[0376] This invention is a system aimed at improving the efficiency of logistics vehicle operations and providing flexible support tailored to the driver's emotional state. The system mainly consists of a server, terminals, and user interaction.

[0377] server

[0378] The server utilizes communication infrastructure to collect real-time data from various sources. This data includes traffic information, weather data, and vehicle status information. The server stores this data in a database and uses a generative AI model to calculate the optimal travel route and schedule. Simultaneously, it analyzes the user's voice and facial expression data using an emotion recognition device to evaluate their emotional state. The server integrates these results and generates prompt messages. For example, it might generate a prompt message such as, "Current traffic conditions are congested. We suggest an alternative route to reach your destination safely and quickly. Also, since your emotions indicate tension, we prioritize relaxing voice guidance."

[0379] terminal

[0380] The terminal is responsible for presenting the driver with route information and emotion-based instructions received from the server. The terminal is equipped with a display that visually shows a map and a speaker for voice guidance. To respond to sudden changes in driving conditions, the terminal can update information in real time and flexibly adjust the guidance.

[0381] User

[0382] The driver, as the user, operates the vehicle according to the route and schedule provided through the terminal. Furthermore, by providing feedback on their emotional state using the terminal, the server can utilize this data for future analysis. For example, by providing feedback such as "I feel more relaxed after completing the trip," it is possible to improve the accuracy of the emotional analysis.

[0383] In this way, the system provides optimal operational support based on real-time information and user sentiment, improving operational efficiency and safety.

[0384] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0385] Step 1:

[0386] The server collects data from various sources, such as traffic conditions, weather information, and vehicle status, in real time via the communication infrastructure. It receives data from traffic sensors, weather APIs, and vehicle sensors as input and stores it in a database. This provides the foundational data needed for subsequent analysis steps.

[0387] Step 2:

[0388] The server inputs the collected data into a generating AI model to calculate the optimal travel route and schedule. This data processing takes into account traffic congestion and weather conditions, while also referencing historical travel data. The output is the recommended travel route and schedule, which is used to generate prompt messages.

[0389] Step 3:

[0390] The server inputs voice data and camera footage into an emotion recognition device to evaluate the user's emotional state. Specifically, it analyzes changes in voice tone and facial expressions to determine the degree of stress and tension. This analysis result is used to adjust guidance according to the user's emotions.

[0391] Step 4:

[0392] The server integrates the output of the generating AI model with the emotion recognition results to generate a prompt message. This prompt message might include something like, "Traffic is currently congested. We suggest an alternative route. Also, because the user's emotion indicates tension, we prioritize relaxing voice guidance." This integration process clarifies the instructions given to the driver.

[0393] Step 5:

[0394] The terminal receives prompt messages and route information sent from the server and presents them to the driver. It receives data from the server as input and outputs it visually and audibly through digital maps and voice guidance. This allows the driver to proceed with the actual operation. Furthermore, the tone and frequency of voice guidance are adjusted according to the driver's emotional state.

[0395] Step 6:

[0396] The user operates the vehicle based on information obtained from their device and provides feedback to the server after the operation is complete. As input, the device records changes in emotions during operation and sends this data to the server. As output, the user's feedback is used for future analysis, enabling more accurate operational support.

[0397] (Application Example 2)

[0398] 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 as the "terminal".

[0399] In logistics, there is a need to improve operational efficiency while reducing driver stress and burden. However, current systems cannot perform real-time sentiment analysis, which can easily increase drivers' mental burden. Furthermore, it is difficult for logistics managers to immediately grasp operational status and drivers' emotional states and provide appropriate support. These challenges need to be addressed.

[0400] 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.

[0401] In this invention, the server includes means for collecting data in real time from multiple information sources via a communication network, means for analyzing the data to generate an optimal route and schedule, and means for analyzing the user's emotions and adjusting the tone and frequency of voice output. This makes it possible to optimize the route, reduce the psychological burden on the driver, and notify the logistics manager of the operational information and emotional state via a visualization device.

[0402] A "communication network" is an infrastructure for sending and receiving data in real time between different devices.

[0403] A "data source" refers to a source that provides various types of data, including traffic conditions and weather information.

[0404] A "route" is the optimal path that a logistics vehicle should take to reach its destination.

[0405] A "schedule" refers to a time-based plan for the operation of logistics vehicles.

[0406] A "display device" is a device that visually presents the route and schedule to the driver.

[0407] "Monitoring" refers to the continuous monitoring of the condition of logistics vehicles.

[0408] "Additional data" refers to further information regarding the operation of logistics vehicles, which is used for analysis.

[0409] "Emotional analysis" is the process of determining a user's mental state from data.

[0410] "Voice output" refers to a system for conveying operational information and announcements through sound.

[0411] A "visualization device" is a device that allows logistics managers to visually check operational information and emotional states.

[0412] The system that realizes this invention first involves a server collecting data in real time from various sources via a communication network. This includes traffic conditions, weather information, and vehicle status. The collected data is stored in a database and analyzed using a generative AI model and an emotion engine. Based on the collected data, the server generates the optimal route and schedule and delivers it to the display devices of the logistics vehicles.

[0413] The display device visually shows the driver the route and schedule transmitted from the server. In addition, the display device also provides guidance through voice output. The tone and frequency of the voice output are adjusted based on the results of an emotional engine's analysis of the driver's emotional state. For example, if the engine analyzes that the driver is in a state of tension, the tone of the voice guidance will be softened and the frequency of navigation will be reduced to promote relaxation.

[0414] Furthermore, logistics managers can use visualization devices to check operational information and drivers' emotional states in real time. This allows for smoother troubleshooting and online support in emergencies. For example, if a driver encounters traffic congestion due to sudden rain and the emotional system detects a high-stress state, this information is immediately shared with the logistics manager, allowing for appropriate advice to be provided.

[0415] The AI ​​generation model can generate prompt messages such as, "Take a deep breath and exhale slowly for 5 seconds. You will be guided to an alternative route, so please don't worry," making it possible to convey appropriate instructions to drivers and logistics managers.

[0416] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0417] Step 1:

[0418] The server collects data in real time from multiple sources, such as traffic conditions, weather information, and vehicle status, via a communication network. This data is obtained from each source as APIs or sensor data and stored in a database within the system. The input is real-time environmental data, and the output is data stored in a structured database.

[0419] Step 2:

[0420] The server analyzes the collected data and uses a generated AI model to calculate the most efficient routes and schedules for logistics vehicles. It receives structured environmental data as input, optimizes routes and schedules based on the AI ​​model, and outputs this in digital format. Specific operations include traffic flow prediction and execution of time optimization algorithms.

[0421] Step 3:

[0422] The terminal receives the route and schedule transmitted from the server and outputs them as visual and audio guidance to the driver's display device. The input is route planning data from the server, and the output is a route map and voice guidance displayed on the user interface. The terminal analyzes navigation information in real time and immediately provides updated information if the route changes.

[0423] Step 4:

[0424] The server uses an emotion engine to analyze the driver's voice and physical data, which are inputs from the terminal, and evaluates the driver's emotional state. The input is voice analysis data, and the output is information about the driver's stress level and emotional state. Based on the emotions, the server generates appropriate voice guidance to reduce stress.

[0425] Step 5:

[0426] Users utilize operational information viewed through their terminals in actual operations, and also input feedback on their own emotional state into the terminal. The input consists of the driver's operational experience and emotional feedback, while the output is data useful for future system improvements. This feedback contributes to continuously improving the accuracy of emotional analysis.

[0427] Step 6:

[0428] The logistics manager's visualization device receives operational status and sentiment information distributed from the server and displays it in the administrator view. Input is operational and sentiment data from the server, and output is a visualized management screen. Its specific functions include real-time status monitoring on the dashboard and emergency response support.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] [Third Embodiment]

[0433] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0434] 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.

[0435] 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).

[0436] 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.

[0437] 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.

[0438] 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).

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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.

[0443] 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.

[0444] 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".

[0445] The smart logistics system of this invention utilizes a high-speed communication network to achieve efficient and environmentally friendly operation of logistics vehicles. The main processing flow and functions are described below.

[0446] server

[0447] The server first receives data acquired in real time from multiple sources. This includes road traffic information, logistics vehicle location information, and weather conditions. The received data is stored in the system's database and analyzed using generating AI. Through this analysis, the optimal route and schedule are generated. The generated route plan is quickly distributed to the terminals of each logistics vehicle.

[0448] terminal

[0449] The terminal notifies the driver of the route plan received from the server. Specifically, it displays the optimal route, departure time, and rest stops along the way. The terminal also monitors fuel levels and vehicle health using sensors installed in the vehicle and sends the necessary data to the server. In this way, the operational status is constantly monitored in real time, and optimization is performed.

[0450] User

[0451] Users can view optimized routes and schedules through their terminals. During their journey, they can use their terminals to follow instructions and adjust their plans as needed. For example, they can receive real-time suggestions for avoiding traffic congestion or making changes due to weather. After their journey, they are expected to provide feedback on the actual results to help improve the system's accuracy.

[0452] Through these processes, various challenges in the logistics industry are resolved, enabling highly efficient and low-cost operations. For example, if a logistics vehicle is traveling on a road with poor visibility due to rain, the server suggests an alternative route and notifies the driver via a terminal. In this way, the system supports safe and efficient logistics operations.

[0453] The following describes the processing flow.

[0454] Step 1:

[0455] The server collects traffic, weather, and location information in real time from multiple data sources. This information is gathered at high speed via a communication network and stored in a secure database.

[0456] Step 2:

[0457] The server analyzes the collected data using AI to calculate the optimal route for logistics vehicles. This analysis includes historical trend analysis and real-time situation assessment, performing optimization according to specific conditions and constraints.

[0458] Step 3:

[0459] The server creates the optimal operating schedule and route based on the analysis results and transmits this information to the terminals of the logistics vehicles. The plan is then delivered in real time using the communication network.

[0460] Step 4:

[0461] The terminal receives the route and schedule transmitted from the server and presents them to the driver. It displays visual route guidance and provides voice guidance as needed.

[0462] Step 5:

[0463] The terminal continuously monitors data from sensors installed in the vehicle, checking fuel levels and engine status. This data is transmitted to a server and used for further operational optimization.

[0464] Step 6:

[0465] The user checks the route plan displayed on the terminal and begins the operation according to that plan. If the situation changes during the operation, adjustments are made in real time according to the instructions on the terminal.

[0466] Step 7:

[0467] After completing a run, users provide feedback via their terminals, reporting actual run results and issues to the server. This feedback will be used to improve the system and enhance its accuracy in the future.

[0468] (Example 1)

[0469] 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."

[0470] In the face of constantly changing road and weather conditions, the safe and efficient operation of logistics vehicles is essential. However, conventional systems face challenges in providing optimal routes due to delays in data collection and analysis. Furthermore, there is a need to accurately grasp the status of logistics vehicles in operation and provide real-time feedback as needed.

[0471] 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.

[0472] In this invention, the server includes means for acquiring data in real time from multiple information sources via a communication medium, means for analyzing the data to generate an optimal route and time plan for a logistics vehicle, and means for transmitting the generated route and time plan to an information terminal of the logistics vehicle. This enables optimal real-time operation management of the logistics vehicle.

[0473] "Communication medium" refers to technical means such as networks used to send and receive data.

[0474] "Information sources" refer to organizations or devices that provide data such as traffic information, location information, and weather information.

[0475] "Real-time" refers to a state in which information is acquired, processed, and distributed almost instantly.

[0476] "Data" refers to the information necessary to optimize the operation of logistics vehicles.

[0477] "Analysis" refers to the process of analyzing acquired data and extracting useful information.

[0478] "Logistical transport vehicles" refer to vehicles used for transporting goods.

[0479] "Route" refers to the path that a moving object should follow when traveling to its destination.

[0480] "Time planning" refers to the allocation and schedule of time from the start to the end of an operation.

[0481] An "information terminal" refers to a device installed on a mobile logistics vehicle for receiving operational plans and monitoring its status.

[0482] A "detection device" refers to a sensor used to measure the state of the environment.

[0483] An "imaging device" refers to a camera used to acquire visual information.

[0484] "Road conditions" refers to the road surface and traffic conditions in the direction of travel.

[0485] "Weather information" refers to data related to weather conditions.

[0486] "Deviation" refers to going off-plan or deviating from a set route or plan.

[0487] "Notification" refers to the act of informing a user of specific information.

[0488] This invention provides a system that utilizes a high-speed and stable communication medium to receive and analyze diverse data in real time, in order to optimize the operation of mobile logistics vehicles. This system consists of a server, terminals, and users, each playing a specific role.

[0489] server

[0490] The server collects various data in real time via communication media, including road traffic data, location information of moving goods, and weather information. Based on this data stored in the system's database, analysis is performed using a generative AI model. The AI ​​model is trained using machine learning frameworks such as TensorFlow and PyTorch, and automatically generates the optimal route and time plan.

[0491] For example, if a logistics vehicle determines that it cannot be safely transported via its usual route due to heavy rain, the server will immediately generate and propose an alternative route.

[0492] Examples of prompts to input into a generative AI model:

[0493] "Use traffic information, location information, and weather information to generate the optimal logistics route and propose a schedule that considers safety and efficiency."

[0494] terminal

[0495] The terminal transmits the optimized route plan received from the server to the driver. Furthermore, it has the function of constantly monitoring data such as fuel level and vehicle status via sensors installed in the vehicle and transmitting this data to the server. This enables immediate responses and adjustments to the plan in response to the situation during operation.

[0496] User

[0497] The user (driver) operates based on information provided by the terminal. By operating according to real-time route information, efficient and safe operation is possible. In addition, performance data can be fed back after the operation is completed, contributing to system improvements. This will further improve the accuracy of routes in subsequent operations.

[0498] In this way, the present invention provides an effective means for safely and efficiently operating logistics vehicles.

[0499] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0500] Step 1:

[0501] The server receives data in real time from multiple sources using communication media. It takes traffic information, location information, and weather information as input and stores this data in a database. API calls are used to receive data, which is updated every minute. For example, based on input data obtained from a traffic information API, the server adds the current congestion status of major roads to the database.

[0502] Step 2:

[0503] The server performs analysis using an AI model based on data stored in the database. It inputs acquired road traffic data, location data, and weather data into the AI ​​model to generate the optimal route and travel schedule. Data processing includes data cleaning and preprocessing using Python, and the output is the calculated route and schedule. For example, the AI ​​model quantifies the impact of weather and predicts the travel time of alternative routes, taking this into account.

[0504] Step 3:

[0505] The server sends the generated optimal route and schedule to the terminal. Here, the generated plan is serialized in JSON format and transferred over the network to the information terminal of the logistics vehicle. For example, the server automatically sends the new operating schedule to all logistics vehicles for the day at 5 a.m.

[0506] Step 4:

[0507] The terminal notifies the driver of the route plan received from the server. It outputs the received route and schedule data by displaying it on the terminal's screen. Furthermore, it monitors fuel levels and engine health through its built-in sensors and transmits this data to the server. For example, the terminal displays an alert to the driver when the fuel level falls below a certain level.

[0508] Step 5:

[0509] The user (driver) operates based on information provided on the terminal. Road conditions and route information obtained from the terminal are used as input for operation, and upon completion of the operation, the results are input as feedback to the terminal. This is recorded as output and stored on the server as reference data for the next operation. For example, the driver can avoid congestion by selecting a suggested alternative route based on real-time road information.

[0510] (Application Example 1)

[0511] 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."

[0512] In the logistics industry, improving operational efficiency and ensuring sustainability are crucial challenges. In particular, route optimization that responds to real-time environmental changes is required, but conventional systems struggle to achieve this effectively. This often leads to problems such as transportation delays, increased costs, and greater environmental impact.

[0513] 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.

[0514] In this invention, the server includes means for collecting data in real time from multiple information sources via a communication network, means for analyzing the data to generate an optimal route and plan for logistics equipment, and means for distributing the generated route and plan to terminals of the logistics equipment. This enables real-time monitoring of operational status and dynamic route adjustment.

[0515] A "communication network" is an infrastructure for exchanging data between multiple devices that are geographically separated.

[0516] "Information source" refers to the recipient or device used to obtain the necessary data.

[0517] "Real-time" is a concept that indicates that data and information are updated instantaneously.

[0518] "Means of analyzing data" refer to devices and technologies that extract useful information based on acquired data and perform processing to support decision-making.

[0519] "Logistics equipment" refers to vehicles and machinery used to transport goods and products.

[0520] "Operating route" refers to the optimal route for traveling between designated points.

[0521] "Planning" refers to designing detailed procedures for operations or activities to be carried out in order to achieve a specific objective.

[0522] A "terminal" is a device used by users to input, display, and manage digital information.

[0523] "Environmental information" refers to data about the surrounding conditions when logistics equipment is in motion.

[0524] "Dynamic route adjustment" is a technique that makes immediate changes to optimize the route based on real-time data.

[0525] In an embodiment of this invention, a server plays a central role. The server collects real-time data from multiple sources, such as traffic information, weather information, and location information of logistics equipment, via a communication network. The collected data is stored in the system's database and analyzed using a generative AI model. This analysis generates the optimal route and plan. The generated route and plan are then quickly distributed to the terminals of each logistics device.

[0526] The terminal notifies the user of the route and plan received from the server via a display device. This display device is often implemented on a smartphone or tablet. The terminal also has the function of sending data on fuel status and equipment health obtained from sensors installed on the logistics equipment to the server. This allows for constant monitoring of the logistics equipment's real-time operation status and enables dynamic route adjustments as needed.

[0527] For example, if sudden weather changes or traffic congestion are predicted along a designated route, the server immediately generates a new, optimal route and notifies the user of the revised plan via their terminal. This makes it possible to maximize operational efficiency while minimizing environmental impact.

[0528] An example of a prompt for a generating AI model is: "Consider the weather conditions for the next 24 hours and generate the optimal route between the following points: origin A, intermediate point B, destination C." This prompt allows the AI ​​to quickly provide a suitable route.

[0529] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0530] Step 1:

[0531] The server receives real-time data such as traffic information, weather data, and location information of logistics equipment as input data collected from multiple sources via a communication network. This information is stored in a database. Protocols such as APIs and WebSockets are used for data collection, and Python and SQL are used for data organization.

[0532] Step 2:

[0533] The server inputs data stored in the database into an AI model and performs data analysis to generate the optimal route and schedule as output. A deep learning model using TensorFlow is employed for the analysis, making predictions that take into account traffic flow and weather patterns. In this process, the AI ​​analyzes real-time data and calculates the most efficient route.

[0534] Step 3:

[0535] The generated routes and schedules are delivered as output data from the server to the logistics equipment terminals. The terminals receive this information and notify the user of the route and schedule in real time via a display device. The user view is updated via a smartphone app built with React Native.

[0536] Step 4:

[0537] The terminal's sensors monitor the fuel status and health condition of the logistics equipment and transmit this information as input data to the server. This allows the server to constantly monitor the equipment's operating status, perform necessary recalculations, and continuously provide optimal route suggestions.

[0538] Step 5:

[0539] Users can monitor the situation through the terminal's display during operation and adjust the plan as needed. Specifically, when dynamically changing routes in consideration of traffic congestion or weather changes, the system provides instructions based on the alternative routes displayed to the user. This enables efficient and safe operation.

[0540] 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.

[0541] The system of the present invention, in order to optimize the operation of logistics vehicles, collects diverse information via a communication network and generates operating routes and schedules based on that information, and also incorporates an emotion engine that recognizes user emotions. The functions of each component and their interactions are described in detail below.

[0542] server

[0543] The server collects real-time information from a variety of high-speed data sources via a communication network. This includes traffic conditions, weather information, and vehicle status. The collected data is stored in a database and analyzed using generative AI and an emotion engine. The emotion engine analyzes the user's state and detects specific emotions. For example, it can process data obtained from voice input and camera footage to assess the user's stress level. Taking this information into consideration, the server generates the optimal route and schedule, making adjustments based on the emotional state.

[0544] terminal

[0545] The terminal receives information transmitted from the server and presents it to the driver. The route and schedule are displayed visually on the user interface and guided by voice. The tone and frequency of the voice guidance are adjusted based on the user's emotional state. For example, if the user is stressed, the tone of the voice guidance is softened and the frequency is reduced to help the driver relax.

[0546] User

[0547] Users can check the route and schedule via their device and operate the vehicle according to the instructions. Furthermore, by providing feedback on their emotional state through the device, the system can perform even more accurate analysis. For example, after a user safely completes an operation, they can provide feedback on their emotional changes, which can then be used as data for future emotional analysis.

[0548] This invention not only improves the efficiency of conventional logistics operations but also reduces the psychological burden on drivers, enabling highly efficient and safe operations. For example, if sudden traffic congestion occurs during operation, the server calculates alternative routes in real time, and if the emotion engine detects an increase in the user's stress level, it quickly adjusts the guidance to reduce the burden on the user. In this way, the system comprehensively supports logistics operations.

[0549] The following describes the processing flow.

[0550] Step 1:

[0551] The server collects real-time data, including traffic information, weather data, location information, and vehicle operating status, via the communication network. This includes information from sensors and cameras installed on roads and GPS devices installed in vehicles.

[0552] Step 2:

[0553] The device collects audio and video data from the driver using microphones and cameras inside the vehicle. This data is used to analyze the user's emotional state.

[0554] Step 3:

[0555] The server analyzes the collected operational and emotional data. The generative AI optimizes the operational routes, and the emotional engine determines the user's emotional state. For example, it detects signs of stress or fatigue.

[0556] Step 4:

[0557] The server creates an optimized route and schedule based on the analysis results and delivers it to the terminal, taking into account the user's emotional state. If the user is emotionally unstable, the nearest rest stop may be added to the route.

[0558] Step 5:

[0559] The terminal displays the received route and schedule to the driver visually and audibly. The voice guidance is adjusted in tone and tempo according to the driver's emotional state.

[0560] Step 6:

[0561] The user begins operation according to the instructions displayed on the terminal and safely adapts to the operating conditions according to the system's instructions. For example, the route may be changed depending on the degree of road congestion.

[0562] Step 7:

[0563] During operation, the terminal continues to monitor vehicle status and driver sentiment data, and sends feedback to the server as needed. The server uses this information to revise the operation plan in real time and redistribute it to the user.

[0564] Step 8:

[0565] After the operation ends, users provide feedback on the entire operation via their devices, sending data to the server that contributes to improving the accuracy of emotion recognition. This feedback will be used to improve future operation plans.

[0566] (Example 2)

[0567] 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."

[0568] Conventional logistics operation systems can optimize routes based on real-time traffic and weather information, but they cannot take into account the emotional state of drivers, such as their psychological burden. As a result, they have problems ensuring sufficient safety and efficiency in operations. Furthermore, they have problems effectively utilizing information from various sensors and not being able to cope with dynamically changing operating environments.

[0569] 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.

[0570] In this invention, the server includes means for collecting current data from various information sources via a communication infrastructure, means for analyzing the data and generating an optimal travel route and plan for the transport vehicle, and means for an emotion recognition device that evaluates the user's emotional state based on the collected data and adjusts the route and guidance based on the evaluation results. This improves operational efficiency while simultaneously enabling flexible support tailored to the driver's emotional state.

[0571] "Communication infrastructure" refers to the entire basic structure for sending and receiving information, including networks and servers.

[0572] "Diverse information sources" refers to multiple data providers that offer information such as traffic conditions, weather data, and vehicle sensor data.

[0573] "Data analysis" refers to the computational process used to derive the optimal travel route and schedule based on collected information.

[0574] A "travel route" refers to the recommended path for a logistics vehicle to travel to its destination.

[0575] "Plan" refers to a schedule of physical movement, including the date, time, sequence, and method.

[0576] "Device" refers to a terminal device that has functions such as displaying information or providing voice guidance.

[0577] An "emotion recognition device" refers to a system that analyzes a user's emotions from audio and video and evaluates their emotional state.

[0578] A "warning" refers to a message that detects a deviation from a set travel path and, if necessary, provides a warning.

[0579] A "recalculated route" refers to a newly generated alternative route to accommodate unexpected events or changes.

[0580] This invention is a system aimed at improving the efficiency of logistics vehicle operations and providing flexible support tailored to the driver's emotional state. The system mainly consists of a server, terminals, and user interaction.

[0581] server

[0582] The server utilizes communication infrastructure to collect real-time data from various sources. This data includes traffic information, weather data, and vehicle status information. The server stores this data in a database and uses a generative AI model to calculate the optimal travel route and schedule. Simultaneously, it analyzes the user's voice and facial expression data using an emotion recognition device to evaluate their emotional state. The server integrates these results and generates prompt messages. For example, it might generate a prompt message such as, "Current traffic conditions are congested. We suggest an alternative route to reach your destination safely and quickly. Also, since your emotions indicate tension, we prioritize relaxing voice guidance."

[0583] terminal

[0584] The terminal is responsible for presenting the driver with route information and emotion-based instructions received from the server. The terminal is equipped with a display that visually shows a map and a speaker for voice guidance. To respond to sudden changes in driving conditions, the terminal can update information in real time and flexibly adjust the guidance.

[0585] User

[0586] The driver, as the user, operates the vehicle according to the route and schedule provided through the terminal. Furthermore, by providing feedback on their emotional state using the terminal, the server can utilize this data for future analysis. For example, by providing feedback such as "I feel more relaxed after completing the trip," it is possible to improve the accuracy of the emotional analysis.

[0587] In this way, the system provides optimal operational support based on real-time information and user sentiment, improving operational efficiency and safety.

[0588] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0589] Step 1:

[0590] The server collects data from various sources, such as traffic conditions, weather information, and vehicle status, in real time via the communication infrastructure. It receives data from traffic sensors, weather APIs, and vehicle sensors as input and stores it in a database. This provides the foundational data needed for subsequent analysis steps.

[0591] Step 2:

[0592] The server inputs the collected data into a generating AI model to calculate the optimal travel route and schedule. This data processing takes into account traffic congestion and weather conditions, while also referencing historical travel data. The output is the recommended travel route and schedule, which is used to generate prompt messages.

[0593] Step 3:

[0594] The server inputs voice data and camera footage into an emotion recognition device to evaluate the user's emotional state. Specifically, it analyzes changes in voice tone and facial expressions to determine the degree of stress and tension. This analysis result is used to adjust guidance according to the user's emotions.

[0595] Step 4:

[0596] The server integrates the output of the generating AI model with the emotion recognition results to generate a prompt message. This prompt message might include something like, "Traffic is currently congested. We suggest an alternative route. Also, because the user's emotion indicates tension, we prioritize relaxing voice guidance." This integration process clarifies the instructions given to the driver.

[0597] Step 5:

[0598] The terminal receives prompt messages and route information sent from the server and presents them to the driver. It receives data from the server as input and outputs it visually and audibly through digital maps and voice guidance. This allows the driver to proceed with the actual operation. Furthermore, the tone and frequency of voice guidance are adjusted according to the driver's emotional state.

[0599] Step 6:

[0600] The user operates the vehicle based on information obtained from their device and provides feedback to the server after the operation is complete. As input, the device records changes in emotions during operation and sends this data to the server. As output, the user's feedback is used for future analysis, enabling more accurate operational support.

[0601] (Application Example 2)

[0602] 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."

[0603] In logistics, there is a need to improve operational efficiency while reducing driver stress and burden. However, current systems cannot perform real-time sentiment analysis, which can easily increase drivers' mental burden. Furthermore, it is difficult for logistics managers to immediately grasp operational status and drivers' emotional states and provide appropriate support. These challenges need to be addressed.

[0604] 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.

[0605] In this invention, the server includes means for collecting data in real time from multiple information sources via a communication network, means for analyzing the data to generate an optimal route and schedule, and means for analyzing the user's emotions and adjusting the tone and frequency of voice output. This makes it possible to optimize the route, reduce the psychological burden on the driver, and notify the logistics manager of the operational information and emotional state via a visualization device.

[0606] A "communication network" is an infrastructure for sending and receiving data in real time between different devices.

[0607] A "data source" refers to a source that provides various types of data, including traffic conditions and weather information.

[0608] A "route" is the optimal path that a logistics vehicle should take to reach its destination.

[0609] A "schedule" refers to a time-based plan for the operation of logistics vehicles.

[0610] A "display device" is a device that visually presents the route and schedule to the driver.

[0611] "Monitoring" refers to the continuous monitoring of the condition of logistics vehicles.

[0612] "Additional data" refers to further information regarding the operation of logistics vehicles, which is used for analysis.

[0613] "Emotional analysis" is the process of determining a user's mental state from data.

[0614] "Voice output" refers to a system for conveying operational information and announcements through sound.

[0615] A "visualization device" is a device that allows logistics managers to visually check operational information and emotional states.

[0616] The system that realizes this invention first involves a server collecting data in real time from various sources via a communication network. This includes traffic conditions, weather information, and vehicle status. The collected data is stored in a database and analyzed using a generative AI model and an emotion engine. Based on the collected data, the server generates the optimal route and schedule and delivers it to the display devices of the logistics vehicles.

[0617] The display device visually shows the driver the route and schedule transmitted from the server. In addition, the display device also provides guidance through voice output. The tone and frequency of the voice output are adjusted based on the results of an emotional engine's analysis of the driver's emotional state. For example, if the engine analyzes that the driver is in a state of tension, the tone of the voice guidance will be softened and the frequency of navigation will be reduced to promote relaxation.

[0618] Furthermore, logistics managers can use visualization devices to check operational information and drivers' emotional states in real time. This allows for smoother troubleshooting and online support in emergencies. For example, if a driver encounters traffic congestion due to sudden rain and the emotional system detects a high-stress state, this information is immediately shared with the logistics manager, allowing for appropriate advice to be provided.

[0619] The AI ​​generation model can generate prompt messages such as, "Take a deep breath and exhale slowly for 5 seconds. You will be guided to an alternative route, so please don't worry," making it possible to convey appropriate instructions to drivers and logistics managers.

[0620] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0621] Step 1:

[0622] The server collects data in real time from multiple sources, such as traffic conditions, weather information, and vehicle status, via a communication network. This data is obtained from each source as APIs or sensor data and stored in a database within the system. The input is real-time environmental data, and the output is data stored in a structured database.

[0623] Step 2:

[0624] The server analyzes the collected data and uses a generated AI model to calculate the most efficient routes and schedules for logistics vehicles. It receives structured environmental data as input, optimizes routes and schedules based on the AI ​​model, and outputs this in digital format. Specific operations include traffic flow prediction and execution of time optimization algorithms.

[0625] Step 3:

[0626] The terminal receives the route and schedule transmitted from the server and outputs them as visual and audio guidance to the driver's display device. The input is route planning data from the server, and the output is a route map and voice guidance displayed on the user interface. The terminal analyzes navigation information in real time and immediately provides updated information if the route changes.

[0627] Step 4:

[0628] The server uses an emotion engine to analyze the driver's voice and physical data, which are inputs from the terminal, and evaluates the driver's emotional state. The input is voice analysis data, and the output is information about the driver's stress level and emotional state. Based on the emotions, the server generates appropriate voice guidance to reduce stress.

[0629] Step 5:

[0630] Users utilize operational information viewed through their terminals in actual operations, and also input feedback on their own emotional state into the terminal. The input consists of the driver's operational experience and emotional feedback, while the output is data useful for future system improvements. This feedback contributes to continuously improving the accuracy of emotional analysis.

[0631] Step 6:

[0632] The logistics manager's visualization device receives operational status and sentiment information distributed from the server and displays it in the administrator view. Input is operational and sentiment data from the server, and output is a visualized management screen. Its specific functions include real-time status monitoring on the dashboard and emergency response support.

[0633] 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.

[0634] 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.

[0635] 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.

[0636] [Fourth Embodiment]

[0637] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0638] 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.

[0639] 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).

[0640] 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.

[0641] 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.

[0642] 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).

[0643] 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.

[0644] 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.

[0645] 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.

[0646] 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.

[0647] 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.

[0648] 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.

[0649] 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".

[0650] The smart logistics system of this invention utilizes a high-speed communication network to achieve efficient and environmentally friendly operation of logistics vehicles. The main processing flow and functions are described below.

[0651] server

[0652] The server first receives data acquired in real time from multiple sources. This includes road traffic information, logistics vehicle location information, and weather conditions. The received data is stored in the system's database and analyzed using generating AI. Through this analysis, the optimal route and schedule are generated. The generated route plan is quickly distributed to the terminals of each logistics vehicle.

[0653] terminal

[0654] The terminal notifies the driver of the route plan received from the server. Specifically, it displays the optimal route, departure time, and rest stops along the way. The terminal also monitors fuel levels and vehicle health using sensors installed in the vehicle and sends the necessary data to the server. In this way, the operational status is constantly monitored in real time, and optimization is performed.

[0655] User

[0656] Users can view optimized routes and schedules through their terminals. During their journey, they can use their terminals to follow instructions and adjust their plans as needed. For example, they can receive real-time suggestions for avoiding traffic congestion or making changes due to weather. After their journey, they are expected to provide feedback on the actual results to help improve the system's accuracy.

[0657] Through these processes, various challenges in the logistics industry are resolved, enabling highly efficient and low-cost operations. For example, if a logistics vehicle is traveling on a road with poor visibility due to rain, the server suggests an alternative route and notifies the driver via a terminal. In this way, the system supports safe and efficient logistics operations.

[0658] The following describes the processing flow.

[0659] Step 1:

[0660] The server collects traffic, weather, and location information in real time from multiple data sources. This information is gathered at high speed via a communication network and stored in a secure database.

[0661] Step 2:

[0662] The server analyzes the collected data using AI to calculate the optimal route for logistics vehicles. This analysis includes historical trend analysis and real-time situation assessment, performing optimization according to specific conditions and constraints.

[0663] Step 3:

[0664] The server creates the optimal operating schedule and route based on the analysis results and transmits this information to the terminals of the logistics vehicles. The plan is then delivered in real time using the communication network.

[0665] Step 4:

[0666] The terminal receives the route and schedule transmitted from the server and presents them to the driver. It displays visual route guidance and provides voice guidance as needed.

[0667] Step 5:

[0668] The terminal continuously monitors data from sensors installed in the vehicle, checking fuel levels and engine status. This data is transmitted to a server and used for further operational optimization.

[0669] Step 6:

[0670] The user checks the route plan displayed on the terminal and begins the operation according to that plan. If the situation changes during the operation, adjustments are made in real time according to the instructions on the terminal.

[0671] Step 7:

[0672] After completing a run, users provide feedback via their terminals, reporting actual run results and issues to the server. This feedback will be used to improve the system and enhance its accuracy in the future.

[0673] (Example 1)

[0674] 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".

[0675] In the face of constantly changing road and weather conditions, the safe and efficient operation of logistics vehicles is essential. However, conventional systems face challenges in providing optimal routes due to delays in data collection and analysis. Furthermore, there is a need to accurately grasp the status of logistics vehicles in operation and provide real-time feedback as needed.

[0676] 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.

[0677] In this invention, the server includes means for acquiring data in real time from multiple information sources via a communication medium, means for analyzing the data to generate an optimal route and time plan for a logistics vehicle, and means for transmitting the generated route and time plan to an information terminal of the logistics vehicle. This enables optimal real-time operation management of the logistics vehicle.

[0678] "Communication medium" refers to technical means such as networks used to send and receive data.

[0679] "Information sources" refer to organizations or devices that provide data such as traffic information, location information, and weather information.

[0680] "Real-time" refers to a state in which information is acquired, processed, and distributed almost instantly.

[0681] "Data" refers to the information necessary to optimize the operation of logistics vehicles.

[0682] "Analysis" refers to the process of analyzing acquired data and extracting useful information.

[0683] "Logistical transport vehicles" refer to vehicles used for transporting goods.

[0684] "Route" refers to the path that a moving object should follow when traveling to its destination.

[0685] "Time planning" refers to the allocation and schedule of time from the start to the end of an operation.

[0686] An "information terminal" refers to a device installed on a mobile logistics vehicle for receiving operational plans and monitoring its status.

[0687] A "detection device" refers to a sensor used to measure the state of the environment.

[0688] An "imaging device" refers to a camera used to acquire visual information.

[0689] "Road conditions" refers to the road surface and traffic conditions in the direction of travel.

[0690] "Weather information" refers to data related to weather conditions.

[0691] "Deviation" refers to going off-plan or deviating from a set route or plan.

[0692] "Notification" refers to the act of informing a user of specific information.

[0693] This invention provides a system that utilizes a high-speed and stable communication medium to receive and analyze diverse data in real time, in order to optimize the operation of mobile logistics vehicles. This system consists of a server, terminals, and users, each playing a specific role.

[0694] server

[0695] The server collects various data in real time via communication media, including road traffic data, location information of moving goods, and weather information. Based on this data stored in the system's database, analysis is performed using a generative AI model. The AI ​​model is trained using machine learning frameworks such as TensorFlow and PyTorch, and automatically generates the optimal route and time plan.

[0696] For example, if a logistics vehicle determines that it cannot be safely transported via its usual route due to heavy rain, the server will immediately generate and propose an alternative route.

[0697] Examples of prompts to input into a generative AI model:

[0698] "Use traffic information, location information, and weather information to generate the optimal logistics route and propose a schedule that considers safety and efficiency."

[0699] terminal

[0700] The terminal transmits the optimized route plan received from the server to the driver. Furthermore, it has the function of constantly monitoring data such as fuel level and vehicle status via sensors installed in the vehicle and transmitting this data to the server. This enables immediate responses and adjustments to the plan in response to the situation during operation.

[0701] User

[0702] The user (driver) operates based on information provided by the terminal. By operating according to real-time route information, efficient and safe operation is possible. In addition, performance data can be fed back after the operation is completed, contributing to system improvements. This will further improve the accuracy of routes in subsequent operations.

[0703] In this way, the present invention provides an effective means for safely and efficiently operating logistics vehicles.

[0704] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0705] Step 1:

[0706] The server receives data in real time from multiple sources using communication media. It takes traffic information, location information, and weather information as input and stores this data in a database. API calls are used to receive data, which is updated every minute. For example, based on input data obtained from a traffic information API, the server adds the current congestion status of major roads to the database.

[0707] Step 2:

[0708] The server performs analysis using an AI model based on data stored in the database. It inputs acquired road traffic data, location data, and weather data into the AI ​​model to generate the optimal route and travel schedule. Data processing includes data cleaning and preprocessing using Python, and the output is the calculated route and schedule. For example, the AI ​​model quantifies the impact of weather and predicts the travel time of alternative routes, taking this into account.

[0709] Step 3:

[0710] The server sends the generated optimal route and schedule to the terminal. Here, the generated plan is serialized in JSON format and transferred over the network to the information terminal of the logistics vehicle. For example, the server automatically sends the new operating schedule to all logistics vehicles for the day at 5 a.m.

[0711] Step 4:

[0712] The terminal notifies the driver of the route plan received from the server. It outputs the received route and schedule data by displaying it on the terminal's screen. Furthermore, it monitors fuel levels and engine health through its built-in sensors and transmits this data to the server. For example, the terminal displays an alert to the driver when the fuel level falls below a certain level.

[0713] Step 5:

[0714] The user (driver) operates based on information provided on the terminal. Road conditions and route information obtained from the terminal are used as input for operation, and upon completion of the operation, the results are input as feedback to the terminal. This is recorded as output and stored on the server as reference data for the next operation. For example, the driver can avoid congestion by selecting a suggested alternative route based on real-time road information.

[0715] (Application Example 1)

[0716] 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".

[0717] In the logistics industry, improving operational efficiency and ensuring sustainability are crucial challenges. In particular, route optimization that responds to real-time environmental changes is required, but conventional systems struggle to achieve this effectively. This often leads to problems such as transportation delays, increased costs, and greater environmental impact.

[0718] 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.

[0719] In this invention, the server includes means for collecting data in real time from multiple information sources via a communication network, means for analyzing the data to generate an optimal route and plan for logistics equipment, and means for distributing the generated route and plan to terminals of the logistics equipment. This enables real-time monitoring of operational status and dynamic route adjustment.

[0720] A "communication network" is an infrastructure for exchanging data between multiple devices that are geographically separated.

[0721] "Information source" refers to the recipient or device used to obtain the necessary data.

[0722] "Real-time" is a concept that indicates that data and information are updated instantaneously.

[0723] "Means of analyzing data" refer to devices and technologies that extract useful information based on acquired data and perform processing to support decision-making.

[0724] "Logistics equipment" refers to vehicles and machinery used to transport goods and products.

[0725] "Operating route" refers to the optimal route for traveling between designated points.

[0726] "Planning" refers to designing detailed procedures for operations or activities to be carried out in order to achieve a specific objective.

[0727] A "terminal" is a device used by users to input, display, and manage digital information.

[0728] "Environmental information" refers to data about the surrounding conditions when logistics equipment is in motion.

[0729] "Dynamic route adjustment" is a technique that makes immediate changes to optimize the route based on real-time data.

[0730] In an embodiment of this invention, a server plays a central role. The server collects real-time data from multiple sources, such as traffic information, weather information, and location information of logistics equipment, via a communication network. The collected data is stored in the system's database and analyzed using a generative AI model. This analysis generates the optimal route and plan. The generated route and plan are then quickly distributed to the terminals of each logistics device.

[0731] The terminal notifies the user of the route and plan received from the server via a display device. This display device is often implemented on a smartphone or tablet. The terminal also has the function of sending data on fuel status and equipment health obtained from sensors installed on the logistics equipment to the server. This allows for constant monitoring of the logistics equipment's real-time operation status and enables dynamic route adjustments as needed.

[0732] For example, if sudden weather changes or traffic congestion are predicted along a designated route, the server immediately generates a new, optimal route and notifies the user of the revised plan via their terminal. This makes it possible to maximize operational efficiency while minimizing environmental impact.

[0733] An example of a prompt for a generating AI model is: "Consider the weather conditions for the next 24 hours and generate the optimal route between the following points: origin A, intermediate point B, destination C." This prompt allows the AI ​​to quickly provide a suitable route.

[0734] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0735] Step 1:

[0736] The server receives real-time data such as traffic information, weather data, and location information of logistics equipment as input data collected from multiple sources via a communication network. This information is stored in a database. Protocols such as APIs and WebSockets are used for data collection, and Python and SQL are used for data organization.

[0737] Step 2:

[0738] The server inputs data stored in the database into an AI model and performs data analysis to generate the optimal route and schedule as output. A deep learning model using TensorFlow is employed for the analysis, making predictions that take into account traffic flow and weather patterns. In this process, the AI ​​analyzes real-time data and calculates the most efficient route.

[0739] Step 3:

[0740] The generated routes and schedules are delivered as output data from the server to the logistics equipment terminals. The terminals receive this information and notify the user of the route and schedule in real time via a display device. The user view is updated via a smartphone app built with React Native.

[0741] Step 4:

[0742] The terminal's sensors monitor the fuel status and health condition of the logistics equipment and transmit this information as input data to the server. This allows the server to constantly monitor the equipment's operating status, perform necessary recalculations, and continuously provide optimal route suggestions.

[0743] Step 5:

[0744] Users can monitor the situation through the terminal's display during operation and adjust the plan as needed. Specifically, when dynamically changing routes in consideration of traffic congestion or weather changes, the system provides instructions based on the alternative routes displayed to the user. This enables efficient and safe operation.

[0745] 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.

[0746] The system of the present invention, in order to optimize the operation of logistics vehicles, collects diverse information via a communication network and generates operating routes and schedules based on that information, and also incorporates an emotion engine that recognizes user emotions. The functions of each component and their interactions are described in detail below.

[0747] server

[0748] The server collects real-time information from a variety of high-speed data sources via a communication network. This includes traffic conditions, weather information, and vehicle status. The collected data is stored in a database and analyzed using generative AI and an emotion engine. The emotion engine analyzes the user's state and detects specific emotions. For example, it can process data obtained from voice input and camera footage to assess the user's stress level. Taking this information into consideration, the server generates the optimal route and schedule, making adjustments based on the emotional state.

[0749] terminal

[0750] The terminal receives information transmitted from the server and presents it to the driver. The route and schedule are displayed visually on the user interface and guided by voice. The tone and frequency of the voice guidance are adjusted based on the user's emotional state. For example, if the user is stressed, the tone of the voice guidance is softened and the frequency is reduced to help the driver relax.

[0751] User

[0752] Users can check the route and schedule via their device and operate the vehicle according to the instructions. Furthermore, by providing feedback on their emotional state through the device, the system can perform even more accurate analysis. For example, after a user safely completes an operation, they can provide feedback on their emotional changes, which can then be used as data for future emotional analysis.

[0753] This invention not only improves the efficiency of conventional logistics operations but also reduces the psychological burden on drivers, enabling highly efficient and safe operations. For example, if sudden traffic congestion occurs during operation, the server calculates alternative routes in real time, and if the emotion engine detects an increase in the user's stress level, it quickly adjusts the guidance to reduce the burden on the user. In this way, the system comprehensively supports logistics operations.

[0754] The following describes the processing flow.

[0755] Step 1:

[0756] The server collects real-time data, including traffic information, weather data, location information, and vehicle operating status, via the communication network. This includes information from sensors and cameras installed on roads and GPS devices installed in vehicles.

[0757] Step 2:

[0758] The device collects audio and video data from the driver using microphones and cameras inside the vehicle. This data is used to analyze the user's emotional state.

[0759] Step 3:

[0760] The server analyzes the collected operational and emotional data. The generative AI optimizes the operational routes, and the emotional engine determines the user's emotional state. For example, it detects signs of stress or fatigue.

[0761] Step 4:

[0762] The server creates an optimized route and schedule based on the analysis results and delivers it to the terminal, taking into account the user's emotional state. If the user is emotionally unstable, the nearest rest stop may be added to the route.

[0763] Step 5:

[0764] The terminal displays the received route and schedule to the driver visually and audibly. The voice guidance is adjusted in tone and tempo according to the driver's emotional state.

[0765] Step 6:

[0766] The user begins operation according to the instructions displayed on the terminal and safely adapts to the operating conditions according to the system's instructions. For example, the route may be changed depending on the degree of road congestion.

[0767] Step 7:

[0768] During operation, the terminal continues to monitor vehicle status and driver sentiment data, and sends feedback to the server as needed. The server uses this information to revise the operation plan in real time and redistribute it to the user.

[0769] Step 8:

[0770] After the operation ends, users provide feedback on the entire operation via their devices, sending data to the server that contributes to improving the accuracy of emotion recognition. This feedback will be used to improve future operation plans.

[0771] (Example 2)

[0772] 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".

[0773] Conventional logistics operation systems can optimize routes based on real-time traffic and weather information, but they cannot take into account the emotional state of drivers, such as their psychological burden. As a result, they have problems ensuring sufficient safety and efficiency in operations. Furthermore, they have problems effectively utilizing information from various sensors and not being able to cope with dynamically changing operating environments.

[0774] 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.

[0775] In this invention, the server includes means for collecting current data from various information sources via a communication infrastructure, means for analyzing the data and generating an optimal travel route and plan for the transport vehicle, and means for an emotion recognition device that evaluates the user's emotional state based on the collected data and adjusts the route and guidance based on the evaluation results. This improves operational efficiency while simultaneously enabling flexible support tailored to the driver's emotional state.

[0776] "Communication infrastructure" refers to the entire basic structure for sending and receiving information, including networks and servers.

[0777] "Diverse information sources" refers to multiple data providers that offer information such as traffic conditions, weather data, and vehicle sensor data.

[0778] "Data analysis" refers to the computational process used to derive the optimal travel route and schedule based on collected information.

[0779] A "travel route" refers to the recommended path for a logistics vehicle to travel to its destination.

[0780] "Plan" refers to a schedule of physical movement, including the date, time, sequence, and method.

[0781] "Device" refers to a terminal device that has functions such as displaying information or providing voice guidance.

[0782] An "emotion recognition device" refers to a system that analyzes a user's emotions from audio and video and evaluates their emotional state.

[0783] A "warning" refers to a message that detects a deviation from a set travel path and, if necessary, provides a warning.

[0784] A "recalculated route" refers to a newly generated alternative route to accommodate unexpected events or changes.

[0785] This invention is a system aimed at improving the efficiency of logistics vehicle operations and providing flexible support tailored to the driver's emotional state. The system mainly consists of a server, terminals, and user interaction.

[0786] server

[0787] The server utilizes communication infrastructure to collect real-time data from various sources. This data includes traffic information, weather data, and vehicle status information. The server stores this data in a database and uses a generative AI model to calculate the optimal travel route and schedule. Simultaneously, it analyzes the user's voice and facial expression data using an emotion recognition device to evaluate their emotional state. The server integrates these results and generates prompt messages. For example, it might generate a prompt message such as, "Current traffic conditions are congested. We suggest an alternative route to reach your destination safely and quickly. Also, since your emotions indicate tension, we prioritize relaxing voice guidance."

[0788] terminal

[0789] The terminal is responsible for presenting the driver with route information and emotion-based instructions received from the server. The terminal is equipped with a display that visually shows a map and a speaker for voice guidance. To respond to sudden changes in driving conditions, the terminal can update information in real time and flexibly adjust the guidance.

[0790] User

[0791] The driver, as the user, operates the vehicle according to the route and schedule provided through the terminal. Furthermore, by providing feedback on their emotional state using the terminal, the server can utilize this data for future analysis. For example, by providing feedback such as "I feel more relaxed after completing the trip," it is possible to improve the accuracy of the emotional analysis.

[0792] In this way, the system provides optimal operational support based on real-time information and user sentiment, improving operational efficiency and safety.

[0793] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0794] Step 1:

[0795] The server collects data from various sources, such as traffic conditions, weather information, and vehicle status, in real time via the communication infrastructure. It receives data from traffic sensors, weather APIs, and vehicle sensors as input and stores it in a database. This provides the foundational data needed for subsequent analysis steps.

[0796] Step 2:

[0797] The server inputs the collected data into a generating AI model to calculate the optimal travel route and schedule. This data processing takes into account traffic congestion and weather conditions, while also referencing historical travel data. The output is the recommended travel route and schedule, which is used to generate prompt messages.

[0798] Step 3:

[0799] The server inputs voice data and camera footage into an emotion recognition device to evaluate the user's emotional state. Specifically, it analyzes changes in voice tone and facial expressions to determine the degree of stress and tension. This analysis result is used to adjust guidance according to the user's emotions.

[0800] Step 4:

[0801] The server integrates the output of the generating AI model with the emotion recognition results to generate a prompt message. This prompt message might include something like, "Traffic is currently congested. We suggest an alternative route. Also, because the user's emotion indicates tension, we prioritize relaxing voice guidance." This integration process clarifies the instructions given to the driver.

[0802] Step 5:

[0803] The terminal receives prompt messages and route information sent from the server and presents them to the driver. It receives data from the server as input and outputs it visually and audibly through digital maps and voice guidance. This allows the driver to proceed with the actual operation. Furthermore, the tone and frequency of voice guidance are adjusted according to the driver's emotional state.

[0804] Step 6:

[0805] The user operates the vehicle based on information obtained from their device and provides feedback to the server after the operation is complete. As input, the device records changes in emotions during operation and sends this data to the server. As output, the user's feedback is used for future analysis, enabling more accurate operational support.

[0806] (Application Example 2)

[0807] 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".

[0808] In logistics, there is a need to improve operational efficiency while reducing driver stress and burden. However, current systems cannot perform real-time sentiment analysis, which can easily increase drivers' mental burden. Furthermore, it is difficult for logistics managers to immediately grasp operational status and drivers' emotional states and provide appropriate support. These challenges need to be addressed.

[0809] 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.

[0810] In this invention, the server includes means for collecting data in real time from multiple information sources via a communication network, means for analyzing the data to generate an optimal route and schedule, and means for analyzing the user's emotions and adjusting the tone and frequency of voice output. This makes it possible to optimize the route, reduce the psychological burden on the driver, and notify the logistics manager of the operational information and emotional state via a visualization device.

[0811] A "communication network" is an infrastructure for sending and receiving data in real time between different devices.

[0812] A "data source" refers to a source that provides various types of data, including traffic conditions and weather information.

[0813] A "route" is the optimal path that a logistics vehicle should take to reach its destination.

[0814] A "schedule" refers to a time-based plan for the operation of logistics vehicles.

[0815] A "display device" is a device that visually presents the route and schedule to the driver.

[0816] "Monitoring" refers to the continuous monitoring of the condition of logistics vehicles.

[0817] "Additional data" refers to further information regarding the operation of logistics vehicles, which is used for analysis.

[0818] "Emotional analysis" is the process of determining a user's mental state from data.

[0819] "Voice output" refers to a system for conveying operational information and announcements through sound.

[0820] A "visualization device" is a device that allows logistics managers to visually check operational information and emotional states.

[0821] The system that realizes this invention first involves a server collecting data in real time from various sources via a communication network. This includes traffic conditions, weather information, and vehicle status. The collected data is stored in a database and analyzed using a generative AI model and an emotion engine. Based on the collected data, the server generates the optimal route and schedule and delivers it to the display devices of the logistics vehicles.

[0822] The display device visually shows the driver the route and schedule transmitted from the server. In addition, the display device also provides guidance through voice output. The tone and frequency of the voice output are adjusted based on the results of an emotional engine's analysis of the driver's emotional state. For example, if the engine analyzes that the driver is in a state of tension, the tone of the voice guidance will be softened and the frequency of navigation will be reduced to promote relaxation.

[0823] Furthermore, logistics managers can use visualization devices to check operational information and drivers' emotional states in real time. This allows for smoother troubleshooting and online support in emergencies. For example, if a driver encounters traffic congestion due to sudden rain and the emotional system detects a high-stress state, this information is immediately shared with the logistics manager, allowing for appropriate advice to be provided.

[0824] The AI ​​generation model can generate prompt messages such as, "Take a deep breath and exhale slowly for 5 seconds. You will be guided to an alternative route, so please don't worry," making it possible to convey appropriate instructions to drivers and logistics managers.

[0825] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0826] Step 1:

[0827] The server collects data in real time from multiple sources, such as traffic conditions, weather information, and vehicle status, via a communication network. This data is obtained from each source as APIs or sensor data and stored in a database within the system. The input is real-time environmental data, and the output is data stored in a structured database.

[0828] Step 2:

[0829] The server analyzes the collected data and uses a generated AI model to calculate the most efficient routes and schedules for logistics vehicles. It receives structured environmental data as input, optimizes routes and schedules based on the AI ​​model, and outputs this in digital format. Specific operations include traffic flow prediction and execution of time optimization algorithms.

[0830] Step 3:

[0831] The terminal receives the route and schedule transmitted from the server and outputs them as visual and audio guidance to the driver's display device. The input is route planning data from the server, and the output is a route map and voice guidance displayed on the user interface. The terminal analyzes navigation information in real time and immediately provides updated information if the route changes.

[0832] Step 4:

[0833] The server uses an emotion engine to analyze the driver's voice and physical data, which are inputs from the terminal, and evaluates the driver's emotional state. The input is voice analysis data, and the output is information about the driver's stress level and emotional state. Based on the emotions, the server generates appropriate voice guidance to reduce stress.

[0834] Step 5:

[0835] Users utilize operational information viewed through their terminals in actual operations, and also input feedback on their own emotional state into the terminal. The input consists of the driver's operational experience and emotional feedback, while the output is data useful for future system improvements. This feedback contributes to continuously improving the accuracy of emotional analysis.

[0836] Step 6:

[0837] The logistics manager's visualization device receives operational status and sentiment information distributed from the server and displays it in the administrator view. Input is operational and sentiment data from the server, and output is a visualized management screen. Its specific functions include real-time status monitoring on the dashboard and emergency response support.

[0838] 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.

[0839] 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.

[0840] 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 robot 414.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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."

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0859] The following is further disclosed regarding the embodiments described above.

[0860] (Claim 1)

[0861] A means of collecting data in real time from multiple sources via a communication network,

[0862] A means for analyzing the data to generate the optimal operating route and schedule for logistics vehicles,

[0863] A means for distributing the generated route and schedule to the terminals of the logistics vehicles,

[0864] The terminal monitors various conditions of the logistics vehicle and transmits additional data for use in the aforementioned analysis.

[0865] A system that includes this.

[0866] (Claim 2)

[0867] The system according to claim 1, comprising means for analyzing environmental information obtained from multiple sensors and cameras to generate a route that takes into account road surface conditions and weather data.

[0868] (Claim 3)

[0869] The system according to claim 1, further comprising means for issuing a warning and providing a recalculated route when the target vehicle deviates from the set operating route.

[0870] "Example 1"

[0871] (Claim 1)

[0872] A means of acquiring data in real time from multiple information sources via a communication medium,

[0873] A means for analyzing the data to generate an optimal route and time plan for a logistics vehicle,

[0874] Means for transmitting the generated route and time plan to the information terminal of the logistics vehicle,

[0875] The information terminal monitors the status of the logistics vehicle and has means for communicating additional data for the analysis,

[0876] A means of providing information to the driver before commencing operations based on the generated route and time plan,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, comprising means for analyzing environmental information obtained from multiple detection devices and imaging devices and generating a route that takes into account road conditions and weather information.

[0880] (Claim 3)

[0881] The system according to claim 1, further comprising means for notifying and providing a recalculated route when a target logistics object deviates from a set route.

[0882] "Application Example 1"

[0883] (Claim 1)

[0884] A means of collecting data in real time from multiple sources via a communication network,

[0885] A means for analyzing the data to generate the optimal operating route and plan for logistics equipment,

[0886] Means for distributing the generated route and plan to the terminals of the logistics equipment,

[0887] The terminal monitors various states of the logistics equipment and transmits additional data for use in the analysis.

[0888] A means for displaying the generated route on a display device in real time,

[0889] A means for performing dynamic route adjustments that take into account environmental information and weather data during operation using the aforementioned display device,

[0890] A system that includes this.

[0891] (Claim 2)

[0892] The system according to claim 1, comprising means for analyzing environmental information obtained from multiple sensors and imaging devices to generate a route that takes into account surface conditions and weather data.

[0893] (Claim 3)

[0894] The system according to claim 1, further comprising means for issuing a warning and providing a recalculated route when the target device deviates from the set operating route.

[0895] "Example 2 of combining an emotion engine"

[0896] (Claim 1)

[0897] A means of collecting current data from diverse sources via communication infrastructure,

[0898] A means for analyzing the aforementioned data and generating the optimal travel route and plan for transport vehicles,

[0899] Means for distributing the generated route and plan to the equipment of the transport vehicle,

[0900] The device includes means for monitoring various conditions of the transport vehicle and transmitting additional data for use in the analysis,

[0901] Based on the collected data, the system includes an emotion recognition device that evaluates the user's emotional state, and means for adjusting routes and guidance based on the evaluation results.

[0902] A system that includes this.

[0903] (Claim 2)

[0904] The system according to claim 1, comprising means for analyzing environmental data obtained from various detection and imaging devices and generating a path that takes into account the ground surface conditions and meteorological data.

[0905] (Claim 3)

[0906] The system according to claim 1, further comprising means for issuing a warning and providing a recalculated route when the target vehicle deviates from a set travel path.

[0907] "Application example 2 of combining emotional engines"

[0908] (Claim 1)

[0909] A means of collecting data in real time from multiple sources via a communication network,

[0910] A means for analyzing the data to generate the optimal operating route and schedule for logistics vehicles,

[0911] Means for distributing the generated route and schedule to the display device of the logistics vehicle,

[0912] The display device monitors various conditions of the logistics vehicle and transmits additional data for use in the analysis.

[0913] A means of analyzing user emotions and adjusting the tone and frequency of voice output,

[0914] A means of displaying operational information to a visualization device for logistics managers and notifying users of their emotional state,

[0915] A system that includes this.

[0916] (Claim 2)

[0917] The system according to claim 1, comprising means for analyzing environmental information obtained from multiple sensors and recording devices to generate a route that takes into account road surface conditions and weather data.

[0918] (Claim 3)

[0919] The system according to claim 1, comprising means for issuing a warning and providing a recalculated route when a target vehicle deviates from a set operating route, and means for notifying the operations manager. [Explanation of Symbols]

[0920] 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 means of collecting data in real time from multiple sources via a communication network, A means for analyzing the data to generate the optimal operating route and schedule for logistics vehicles, A means for distributing the generated route and schedule to the terminals of the logistics vehicles, The terminal monitors various conditions of the logistics vehicle and transmits additional data for use in the aforementioned analysis. A system that includes this.

2. The system according to claim 1, comprising means for analyzing environmental information obtained from multiple sensors and cameras to generate a route that takes into account road surface conditions and weather data.

3. The system according to claim 1, further comprising means for issuing a warning and providing a recalculated route when the target vehicle deviates from the set operating route.

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