system

Abstract

We provide the system. [Solution] A processing means that acquires real-time location information and traffic information of transport vehicles, and executes a program that analyzes this data to generate the optimal transport route, A communication means for providing generated transport route information to the driver of the transport vehicle, A dialogue means that accepts input from the driver and recalculates and updates the transport route as needed, A data storage system that collects feedback from drivers and recipients after delivery is completed and uses it to optimize the next transportation route, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In view of the limitations of the prior art in which it is difficult to achieve efficient logistics under the constraints of new laws with limited delivery times, there is a need for a mechanism in which multiple transportation companies cooperate to optimize delivery. This is required to eliminate delays in local logistics and improve the quality and efficiency of delivery.

Means for Solving the Problems

[0005] According to the present invention, a system is constructed that collects real-time location and traffic information of transport vehicles and provides an optimal transport route using generated AI based on that information. This system communicates with the driver's terminal to provide the latest route information, accepts input from the driver, and recalculates and updates the route if necessary. Furthermore, by collecting feedback from the driver and recipient after delivery is completed and using it to optimize future operations, the system realizes efficient logistics operations.

[0006] "Real-time" refers to a time concept where data and information are processed and updated instantly the moment they are generated.

[0007] "Transport vehicles" refer to vehicles such as automobiles and trucks used to transport goods or people from one point to another.

[0008] "Location information" refers to data that indicates the geographical coordinates of a specific object using GPS or other technologies.

[0009] "Traffic information" refers to all information regarding road conditions, such as road congestion, traffic jams, and the presence or absence of accidents.

[0010] "Analysis" is the process of examining data in detail to clarify its meaning and trends for a specific purpose.

[0011] A "transportation route" refers to the path or route chosen for delivery or transportation.

[0012] "Generative AI" refers to a type of artificial intelligence technology that has the ability to automatically generate the optimal solution based on given data.

[0013] "Communication methods" is a general term for the methods and technologies used to transmit information from a sender to a receiver.

[0014] "Terminal" refers to information processing devices such as computers and smartphones that are directly operated by the user.

[0015] "Input" refers to the act of a user providing some data or instructions to the system or the data used for that.

[0016] "Dialogue means" refers to the functions or technologies that enable two-way communication between the system and the user.

[0017] "Feedback" refers to the response from the user that provides information for evaluating and improving a service or process.

[0018] "Data storage means" refers to a device or system that stores data for a long time and uses it for various purposes.

Brief Description of Drawings

[0019] [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] [[ID=2 fifty]]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] [[ID=forty]]It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [[ID=۴۲]] [Figure 8] [[ID=۴۳]]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. [[ID=۴۴]] [[ID=۴۵]] [Figure 9] [[ID=۴۶]]It shows an emotion map to which multiple emotions are mapped. [[ID=۴۷]] [[ID=۴۸]] [Figure 10]Shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0020] 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.

[0021] First, the language used in the following description will be explained.

[0022] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0023] 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.

[0024] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0025] 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).

[0026] 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."

[0027] [First Embodiment]

[0028] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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".

[0040] In an embodiment of the present invention, a system is provided for determining the efficient route of transport vehicles in real time. The system mainly consists of a server, a terminal, and a user.

[0041] The server receives location information from GPS devices installed in the transport vehicles. It also obtains the latest traffic and weather conditions from external traffic and weather information APIs. This data is analyzed by a generated AI on the server, and the optimal route for each transport vehicle is calculated based on an optimization algorithm. The generated optimal route is then transmitted to the vehicle's terminal.

[0042] The terminal receives optimal route information transmitted from the server and presents it to the driver (user). This is done through visual map displays and voice guidance. The terminal is equipped with a chatbot function as a means of interaction, and can receive questions from the driver. For example, if the driver encounters traffic congestion or unexpected road closures, they can request alternative route suggestions through the terminal.

[0043] Technically, the server can recalculate the route as needed and send the updated route to the terminal. This two-way communication ensures that drivers always follow the optimal route for deliveries. Once a delivery is complete, the terminal sends a delivery completion notification to the server, which includes feedback on the actual arrival time and any problems encountered along the route.

[0044] As a concrete example, consider a scenario where a user has multiple delivery destinations within the Tohoku region on a given day. The server analyzes traffic conditions and weather data for each destination and generates the shortest route. If an unexpected weather change is reported along the way, the server calculates an alternative route in response to the user's request and provides new directions to the terminal. After delivery is complete, the user sends feedback to the server via the terminal, which is used to optimize future routes.

[0045] In this way, the system of the present invention enables efficient logistics operations even under time constraints imposed by legal revisions.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] The server collects real-time data, including location information, from the GPS devices of transport vehicles. It also obtains road congestion information from a traffic information API and the latest weather information from a weather information API. This data is stored in the server's database and used for later analysis.

[0049] Step 2:

[0050] The server analyzes the accumulated data using a generating AI to evaluate the current conditions of each transport vehicle. Here, the optimal transport route is calculated, taking into account the shortest route to the destination, expected traffic conditions, and weather conditions. A route optimization algorithm is used to generate and evaluate multiple route options.

[0051] Step 3:

[0052] The server selects the most efficient route and transmits this information to the vehicle's terminal. This information includes details of the route, estimated arrival time, and points requiring special attention.

[0053] Step 4:

[0054] The terminal displays the received route information to the driver. The driver can check the details of the current route through visual map displays and voice guidance.

[0055] Step 5:

[0056] If a user (driver) encounters an unexpected situation (such as traffic congestion or a road closure) while driving, they can request a new route from the server via their device. This request is made through a chatbot function.

[0057] Step 6:

[0058] The server re-evaluates the data in real time upon request from the driver and recalculates the transport route as needed. Once the new optimal route is determined, it is sent to the terminal.

[0059] Step 7:

[0060] The user (driver) receives the newly provided route guidance and resumes driving based on that information.

[0061] Step 8:

[0062] After delivery is complete, the user sends feedback to the server via their device regarding the actual arrival time and delivery route. This information is used to improve the accuracy of route optimization for future deliveries.

[0063] (Example 1)

[0064] 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."

[0065] There is a need to support drivers in executing deliveries along the optimal route while responding to real-time changes in traffic and weather conditions in transportation operations. However, conventional systems have problems such as difficulty in flexibly responding to sudden changes in traffic information and weather, and the inability to immediately recalculate based on driver input. This leads to challenges such as decreased transportation efficiency and delays.

[0066] 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.

[0067] In this invention, the server includes processing means for analyzing location data received from transport equipment and traffic and air quality information obtained from external information sources, and deriving the optimal transport route based on generative AI technology; communication means for transmitting the derived transport route information to the operator of the transport equipment; and dialogue means for receiving requests from the operator and recalculating and revising the transport route according to the situation. This enables efficient and flexible operation of transport routes that take into account environmental information that changes in real time.

[0068] "Transportation equipment" refers to vehicles and devices used for the purpose of transporting goods, and this includes trucks, buses, and the like.

[0069] "Location data" refers to the current geographical location information of a transport device, which is usually obtained via a GPS device.

[0070] "External information sources" refer to third-party services and databases that provide traffic information and air quality data, and are often accessed via APIs.

[0071] "Traffic conditions" refer to information indicating the degree of road congestion and the presence or absence of traffic jams, and are factors that affect the movement of transportation equipment.

[0072] "Atmospheric conditions" refers to information about weather and climate, including factors that affect the safety and efficiency of transportation.

[0073] "Generative AI technology" refers to technology that uses artificial intelligence to analyze large amounts of data and generate information suitable for a specific purpose.

[0074] A "transportation route" refers to the path or route used by transportation equipment to move from a specific point to its destination.

[0075] "Processing means" refers to functions or devices for analyzing received data and performing necessary calculations.

[0076] "Communication methods" refer to technologies and devices for sending and receiving information, and this includes wireless communication and network communication.

[0077] "Dialogue means" refers to devices or systems that have the function of receiving input from users and responding or performing operations based on that information.

[0078] "Information storage means" refers to technologies and devices that store collected data and retain it for later analysis and use.

[0079] This invention relates to a system that processes various types of information, including location data of transportation equipment, in real time and calculates the optimal transportation route. This system mainly consists of three elements: a server, a terminal, and a user.

[0080] The server first receives location data from GPS devices installed in the transport equipment. It also obtains the latest traffic and weather data from external information sources such as traffic APIs and weather APIs. This data is analyzed using generative AI technology to calculate the optimal transport route, taking traffic information and weather conditions into account. During this process, the server prompts the generative AI model with the message, "Based on the current location and destination, please suggest the shortest route considering traffic and weather." The optimized transport route is then transmitted to the terminal using communication technology.

[0081] The terminal receives transportation route information transmitted from the server and presents it to the user (driver) through visual map displays and voice guidance. The terminal also features a chatbot function as a means of interaction with the driver. If the driver encounters traffic congestion or road closures, the terminal can request the server to calculate alternative routes again.

[0082] The user, acting as the driver, transports the goods according to the route displayed on the terminal. After completing the transport, the driver sends arrival information and feedback about any obstacles encountered along the route to the server via the terminal. This feedback is collected by an information storage system and used to optimize future transport routes.

[0083] As a concrete example, consider a scenario where a driver makes deliveries to multiple locations within a wide area on a given day. In this case, the server analyzes traffic and weather conditions at each location and generates the optimal route. If the weather suddenly changes along the way, the driver can receive a suggestion for a new route via a terminal and continue deliveries. In this way, the system achieves efficient transportation and solves challenges in transportation operations.

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] The server receives current location data from GPS devices installed on the transport equipment. The input is raw location information obtained from the GPS device, which the server receives and processes. The server formats this location data and converts it into a standard format for use in the next step. The output is location data in a parseable format.

[0087] Step 2:

[0088] The server obtains the latest traffic and weather data via external information sources such as traffic and weather APIs. Input is the response from the APIs, typically provided in data formats such as JSON. The server receives this data in real time, extracts the necessary information, and formats it for analysis. The output is situational information combining traffic and weather data.

[0089] Step 3:

[0090] The server uses generative AI technology to analyze location data and contextual information. Standardized location data and organized contextual information are provided to the generative AI model as input. The prompt used is "Based on the current location and destination, suggest the shortest route considering traffic and weather." The output is the optimal transport route calculated by the optimization algorithm.

[0091] Step 4:

[0092] The server transmits the calculated optimal transportation route information to the terminal. This communication utilizes a secure protocol to maintain the consistency and security of the transmitted data. The input is the calculated route information, and the output is the optimal route information received by the terminal.

[0093] Step 5:

[0094] The terminal displays received transportation route information to the user. Input is route information received from the server, and output is a visual map display and voice guidance. The terminal utilizes chatbot functionality to accept additional requests and questions from the user. This allows the terminal to provide users with efficient and safe transportation guidance.

[0095] Step 6:

[0096] If a user encounters a new situation during transport, such as traffic congestion or a sudden change in weather, they can use their terminal to request the server to calculate an alternative route. The input is the user's request information, and the output is the newly calculated transport route information. The server then uses the regenerated AI model to quickly calculate a route suitable for the situation and sends it to the terminal.

[0097] Step 7:

[0098] When a user completes a transportation task, they send a completion notification and feedback from their terminal to the server. The input consists of arrival information upon completion of the task and feedback on any problems encountered. The server receives this information and uses it to improve future route calculations. The output is the improvement information stored in the database.

[0099] (Application Example 1)

[0100] 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."

[0101] In the food delivery industry, delays due to traffic conditions and weather changes are a major challenge, leading to decreased customer satisfaction. Furthermore, selecting efficient delivery routes is difficult, placing a heavy burden on delivery personnel. A system is needed to address these challenges and achieve fast and efficient delivery.

[0102] 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.

[0103] In this invention, the server includes a computing means that acquires location data, traffic data, and weather data of a means of transport in real time, analyzes this data to generate an efficient transport route, an information transmission means that provides the generated transport route information to a person operating the means of transport, and an interaction means that receives requests from the operator and recalculates and updates the transport route as necessary. This makes it possible to deliver via the optimal route at all times.

[0104] "Real-time" refers to a timeframe in which information is processed and presented instantaneously.

[0105] "Transportation means" refers to any device or system used to move goods or people from one point to another.

[0106] "Location data" refers to information that indicates the current geographical coordinates of a specific object or person.

[0107] "Traffic data" refers to information about the flow of vehicles on roads, including conditions such as congestion and accidents.

[0108] "Weather data" refers to information about weather conditions, including temperature, precipitation, and wind speed.

[0109] "Analysis" is a detailed investigation that breaks down data and helps understand its patterns and trends.

[0110] An "efficient transport route" is a route designed to reach the destination safely and quickly in the shortest possible time.

[0111] "Computational means" refers to a device or program that has the ability to process data for a specific purpose.

[0112] "Information transmission means" refers to a process or system for effectively transmitting data or information to a person or device.

[0113] "Means of interaction" refer to the mechanisms or programs that enable humans and systems to communicate.

[0114] "Optimization" is the process of using resources in the most efficient way to achieve a goal.

[0115] The system for implementing the present invention has the ability to acquire location data, traffic data, and weather data of a means of transport in real time, analyze them, and generate the most efficient transport route. The server acquires location data from GPS devices installed on the means of transport and collects traffic and weather data using external data services. This includes services such as Google® Maps API and OpenWeather API. This data is analyzed and optimized using a custom algorithm written in Python. The generated transport route information is transmitted to the user's terminal operating the means of transport via Wi-Fi or a cellular network.

[0116] The terminal provides this information to the user through visual map displays and voice guidance. Users can also ask questions and make requests in real time using the terminal's built-in chatbot function. This interaction allows users to quickly respond to problems or changes that occur during transport. After delivery is complete, the terminal sends feedback from the user and recipient to a server, and this information is used to optimize future transport routes.

[0117] As a concrete example, in food delivery, there is a need for the automatic generation of efficient routes during peak hours. The server reflects traffic congestion and weather changes in real time, helping delivery personnel deliver goods to multiple destinations within a set time. In this case, the food delivery application presents the user with the latest route and notifies them immediately if there are any changes.

[0118] As an example of a specific prompt to the generating AI model, the following is used: "For the concentrated orders in the Shinjuku area, calculate an efficient delivery route based on current traffic conditions and weather data. Provide the optimal route for the next 30 minutes and notify the delivery driver by voice."

[0119] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0120] Step 1:

[0121] The server receives real-time location data from GPS devices installed on the means of transport. The input is geographic coordinates (latitude and longitude) provided by the GPS device. The server extracts this data and stores it temporarily. The output is the current, accurate location data, which is then recognized by the server.

[0122] Step 2:

[0123] The server uses external API services (e.g., Google Maps API, OpenWeather API) to retrieve current and predicted traffic and weather data. The input consists of queries to the APIs, including location data as a parameter. The server analyzes the data retrieved from these APIs to identify traffic congestion and weather conditions. The output provides traffic and weather conditions for a specific area.

[0124] Step 3:

[0125] The server uses a generative AI model to calculate the optimal transport route based on acquired location data, traffic data, and weather data. The input is the dataset obtained in the previous steps. The generative AI model processes this data and calculates the route that maximizes efficiency. The output is the optimized transport route.

[0126] Step 4:

[0127] The server sends the generated optimal transport route to the user's terminal operating the transport. As input, route information is formatted and transmitted via communication. The terminal receives this information and prepares to provide visual and audio guidance to the user. As output, real-time route guidance data is displayed on the terminal.

[0128] Step 5:

[0129] The user performs transportation based on information provided via the terminal. They may also use the chatbot function to request a route recalculation if necessary. Input includes user feedback and additional instructions. The terminal processes these requests and sends new requests to the server. The output is the updated transportation route, if necessary, displayed again on the terminal.

[0130] Step 6:

[0131] Once delivery is complete, the user enters a delivery completion report into the terminal. This input includes information such as confirmation of successful delivery and any problems encountered during transit. The terminal collects this feedback and sends it back to the server. As output, the server accumulates feedback information that will be used for future optimizations.

[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] In embodiments of the present invention, a system is provided that not only determines the efficient route of transport vehicles in real time, but also analyzes the user's emotions and adapts the system's operation accordingly. The system mainly consists of a server, a terminal, a user, and an emotion engine.

[0134] The server is responsible for collecting location, traffic, and weather information related to transport vehicles. This data is analyzed by a generative AI and forms the basis for calculating the optimal transport route. The calculated optimal route information is then transmitted to the terminals of each transport vehicle for use.

[0135] The device has a built-in chatbot function for interacting with the driver (user). Using this function, users can request new routes as needed while driving. Furthermore, the device incorporates an emotion engine that can understand the user's emotional state in real time through speech recognition and text analysis.

[0136] The emotion engine recognizes the user's emotions, which are then sent to the server and used to adjust the optimal route and select the appropriate communication method as needed. For example, if the system determines that the user is feeling stressed or anxious, it can select and provide a calmer voice guidance.

[0137] For example, if a user reports fatigue due to long hours of driving, the system detects this emotion through its emotion engine. The server then prioritizes guiding the user to an immediately available rest area and provides this information to the terminal. If the user is relaxed, the system continues with normal guidance.

[0138] After delivery is complete, feedback, including user emotional data, is stored on the server. This information is used to improve the accuracy of future optimization algorithms, enabling more effective support for operations. As a result, the system of this invention achieves efficient logistics while reducing the psychological stress on drivers.

[0139] The following describes the processing flow.

[0140] Step 1:

[0141] The server collects location information from GPS devices installed in transport vehicles. It also obtains the latest road and weather information via traffic and weather APIs. This data is integrated on the server and used for real-time route optimization.

[0142] Step 2:

[0143] The server analyzes the collected data using generating AI to evaluate the operating conditions of each transport vehicle. This includes a process of selecting the optimal transport route, taking into account the shortest route to the destination, congestion levels, and weather conditions.

[0144] Step 3:

[0145] The server transmits the generated optimal route information to the terminal of each transport vehicle. This information includes specific route details, estimated travel time, and important notes.

[0146] Step 4:

[0147] The terminal receives route information from the server and presents it to the user through a visual map display and voice guidance. The terminal also incorporates an emotion engine that analyzes the user's emotional state while driving based on their input.

[0148] Step 5:

[0149] If a user reports stress or fatigue while driving, the device uses an emotion engine to detect that emotion and sends feedback to the server.

[0150] Step 6:

[0151] The server analyzes the user's emotional state received from the emotion engine and adjusts the route and guidance messages as needed. For example, it might guide the user to a relaxing resting place or switch to a calmer voice guidance.

[0152] Step 7:

[0153] When a user requests a new route, the device sends the request to the server. The server immediately recalculates the route and provides the device with the new directions.

[0154] Step 8:

[0155] After delivery is complete, the user sends arrival time, delivery route, and emotional feedback to the server via their device. This information is stored in the server's database and used to optimize future delivery plans.

[0156] (Example 2)

[0157] 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".

[0158] In modern times, while there is a demand for increased efficiency in transport vehicles, there is a challenge in that operational support that takes into account the psychological state of drivers is not adequately provided. Therefore, there is a need to develop systems that are efficient while reducing driver stress.

[0159] 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.

[0160] This invention includes a server that includes a processing means for acquiring location information, traffic information, and weather information related to transport vehicles in real time and analyzing these multiple pieces of information to generate an optimal transport route; an adaptive means for analyzing the driver's emotions and adaptively changing voice guidance and route guidance based on that; and a data storage means for collecting feedback, including the driver's emotional data, after delivery is completed and utilizing it for optimizing the next transport route. This makes efficient transport possible while reducing the psychological burden on the driver.

[0161] "Real-time" refers to the instantaneous acquisition or processing of information and data without delay.

[0162] A "transport vehicle" is a machine or device used to transport goods or passengers, and generally includes those that primarily travel on roads or railways.

[0163] "Location information" refers to data that indicates the geographical location of a specific object or person, and is usually represented by latitude and longitude.

[0164] "Traffic information" refers to data on vehicle flow, traffic congestion, and road conditions.

[0165] "Weather information" refers to data related to meteorological conditions, including information such as temperature, precipitation, and wind speed.

[0166] "Processing means" refers to a method or apparatus for receiving, processing, analyzing, and interpreting data.

[0167] A "generative AI model" refers to a framework of artificial intelligence that performs pattern recognition and information generation based on large amounts of data.

[0168] A "prompt message" refers to an input sentence used to give instructions or questions to an AI model.

[0169] "Communication means" refers to methods and devices for sending and receiving information.

[0170] "Dialogue means" refers to methods and devices for users and systems to exchange information and instructions.

[0171] "Emotional analysis" refers to estimating a person's psychological state based on information such as audio and text.

[0172] "Adaptive measures" refer to methods or devices for changing or adapting systems or processes according to the situation.

[0173] "Data storage means" refers to methods and devices for storing collected information and data for later use.

[0174] This invention is a system in which a server, terminal, and user interact with each other to improve the efficiency of transport vehicle operations in real time, and to provide support tailored to the driver's psychological state.

[0175] The server first collects necessary data using the vehicle's location information, traffic information, and weather information. GPS devices and various APIs are used for this data collection. For example, traffic information is obtained using the Google Maps API, and weather data is acquired using weather information APIs. Next, the server analyzes this data using a generative AI model to generate the optimal transport route. The generated route information is then transmitted to the driver's terminal via communication means.

[0176] The terminal is a central element supporting interaction with the driver. Chatbot functionality allows drivers to interact with the terminal via voice or text, obtaining information during their journey and issuing instructions for route changes. Furthermore, a built-in emotion analysis engine analyzes the driver's voice and text to estimate their psychological state. For example, it uses a speech recognition API to analyze the driver's tone and detect signs of stress or fatigue.

[0177] If a user reports feeling fatigued due to prolonged driving, the server uses emotional information and a generative AI model to generate a prompt such as, "Guide me to the best route from my current location to the nearest rest stop." Based on this prompt, the driver is provided with appropriate route guidance. If the user is relaxed, the system continues with normal guidance.

[0178] As a result, this system can reduce the psychological burden on drivers and enable efficient transportation.

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

[0180] Step 1:

[0181] The server collects location information of transport vehicles obtained from GPS devices, traffic information from traffic APIs, and weather information from weather APIs. This information is input to the server as raw data and stored in a database. This stored data forms the basis for subsequent analysis and route calculations.

[0182] Step 2:

[0183] The server inputs the collected data into a generating AI model to calculate the optimal transport route. Specifically, it determines the main route from the current vehicle's location to its destination based on location and traffic information, and then adapts that route considering weather conditions. The outputted optimal route information becomes the data that will be transmitted to the driver in the next step.

[0184] Step 3:

[0185] The server sends the generated optimal route information to the driver's terminal. This uses a mechanism that updates the data in real time using a communication protocol. The route information received by the terminal is displayed for the user's reference.

[0186] Step 4:

[0187] The terminal initiates a conversation with the driver, using a chatbot function to accept voice commands and text input from the driver. If the driver requests a new route, the terminal resends the request to the server to prompt a route recalculation. The input at this time is a new route request, and the output is the new route information.

[0188] Step 5:

[0189] The device activates an emotion engine and analyzes voice and text from the driver. This analysis is performed to determine the driver's stress and fatigue levels, and emotional data is output. This helps to understand the driver's psychological state and assists in taking the next steps.

[0190] Step 6:

[0191] The server receives emotion data sent from the terminal and communicates it to the AI ​​model that generates new prompts, thereby formulating appropriate countermeasures. For example, a prompt such as "Provide calm navigation" can be output by the system, enabling it to provide guidance tailored to the driver's psychological state.

[0192] Step 7:

[0193] After delivery is complete, user feedback is sent from the terminal to the server. This feedback includes driver sentiment data and is used as a data accumulation tool. The accumulated data is used to optimize future routes and improve the system.

[0194] (Application Example 2)

[0195] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0196] Optimizing both operational efficiency and worker stress simultaneously in a logistics center is not easy. In particular, proceeding with work while ignoring the emotional state of individual workers can lead to decreased efficiency and increased errors. Therefore, it is necessary to understand workers' emotional states in real time and provide work instructions accordingly.

[0197] 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.

[0198] In this invention, the server includes information processing means that acquire location information and traffic information of transport devices in real time and analyze this data to generate an optimal transport route; emotion recognition means that identify the emotional state based on the user's voice and facial expression data and adjust the guidance method accordingly; and communication means that provide the generated transport route information to the user. This makes it possible to provide optimal work instructions in a logistics center according to the emotional state of the workers.

[0199] "Real-time" is a concept that refers to information and actions being processed immediately and used without delay when needed.

[0200] "Transportation equipment" refers to a machine or system used to transport goods or people to a designated location.

[0201] "Location information" refers to data that indicates the geographical coordinates or location of a specific object.

[0202] "Traffic information" refers to data on road and public transportation congestion and operating conditions.

[0203] "Information processing means" refers to devices and programs used to analyze data and extract and process necessary information.

[0204] "Voice and facial expression data" refers to digital information about the characteristics of the user's voice and facial expressions.

[0205] "Emotional state" refers to the psychological or emotional state that an individual is currently experiencing.

[0206] "Emotion recognition means" refers to technologies and devices that analyze and identify an individual's emotional state based on data such as voice and facial expressions.

[0207] "Communication means" refers to the technology or equipment used to send and receive information.

[0208] "Information storage means" refers to a device or system for securely storing data and information for later use.

[0209] An example of this invention's application is a system designed to improve work efficiency and reduce worker stress in a logistics center. This system aims to provide workers with real-time information using smart glasses.

[0210] The server collects inventory data, shelf location information, and worker location information within the logistics center. This data is analyzed by a generative AI model to generate the optimal picking route. The server also combines this data with environmental data to determine the most efficient work procedure. The specific software used includes OpenAI® as the generative AI model and Amazon Rekognition as the emotion recognition engine.

[0211] The device refers to smart glasses that visualize the generated picking route for the worker and provide voice guidance. The glasses' built-in camera and microphone collect the worker's voice and facial expression data, and analyze their emotional state in real time. Based on their emotions, the system adjusts the work route and suggests breaks to reduce workload.

[0212] For example, when a staff member at a logistics center is picking complex items, if the system detects that the worker is fatigued, the server will immediately adjust the work route and suggest a break. An example of a prompt message would be, "Based on staff emotional data, please suggest the optimal method for adjusting the workload."

[0213] This system makes it possible for workers within the logistics center to perform their tasks efficiently and effectively.

[0214] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0215] Step 1:

[0216] The server collects inventory, shelf locations, and worker location information within the logistics center. Using this data as input, a generative AI model calculates the optimal picking route. As output, customized route information is generated for each worker.

[0217] In terms of specific operations, the process involves querying a database to retrieve the necessary data, then passing that data to a generative AI model for analysis.

[0218] Step 2:

[0219] The smart glasses, acting as the terminal, display the picking route received from the server in the worker's field of vision and also provide voice guidance. The input for this process is route information from the server, and the output is the presentation of information to the worker.

[0220] The specific actions include displaying graphics on a display built into the smart glasses and playing audio from the speaker.

[0221] Step 3:

[0222] The user's voice and facial expressions are captured by the terminal's camera and microphone. This data is used as input by an emotion recognition engine, which analyzes it to identify the worker's emotional state. The output is digital data related to the current emotional state.

[0223] Specifically, the collected audio and video data is sent to an emotion recognition engine in the cloud for analysis.

[0224] Step 4:

[0225] The server receives the worker's emotional state from the emotion recognition engine and uses this information to optimize picking routes and work instructions. The input for this step is emotional state data, and the output is the adjusted work instructions.

[0226] The specific actions include route optimization using algorithms based on sentiment data and the generation of new instruction data.

[0227] Step 5:

[0228] When a user completes a task, they send their feedback to the server. This generates accumulated data that is used to optimize the next picking route. The input is the worker's feedback, and the output is an update of the data used for future optimization.

[0229] The specific actions include displaying a short questionnaire on smart glasses after the work is completed, and sending the responses to a server for storage in a database.

[0230] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0231] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0232] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0233] [Second Embodiment]

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

[0235] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0236] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0237] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0238] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0239] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0240] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0241] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0242] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0243] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0244] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0245] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0246] In an embodiment of the present invention, a system is provided for determining the efficient route of transport vehicles in real time. The system mainly consists of a server, a terminal, and a user.

[0247] The server receives location information from GPS devices installed in the transport vehicles. It also obtains the latest traffic and weather conditions from external traffic and weather information APIs. This data is analyzed by a generated AI on the server, and the optimal route for each transport vehicle is calculated based on an optimization algorithm. The generated optimal route is then transmitted to the vehicle's terminal.

[0248] The terminal receives optimal route information transmitted from the server and presents it to the driver (user). This is done through visual map displays and voice guidance. The terminal is equipped with a chatbot function as a means of interaction, and can receive questions from the driver. For example, if the driver encounters traffic congestion or unexpected road closures, they can request alternative route suggestions through the terminal.

[0249] Technically, the server can recalculate the route as needed and send the updated route to the terminal. This two-way communication ensures that drivers always follow the optimal route for deliveries. Once a delivery is complete, the terminal sends a delivery completion notification to the server, which includes feedback on the actual arrival time and any problems encountered along the route.

[0250] As a concrete example, consider a scenario where a user has multiple delivery destinations within the Tohoku region on a given day. The server analyzes traffic conditions and weather data for each destination and generates the shortest route. If an unexpected weather change is reported along the way, the server calculates an alternative route in response to the user's request and provides new directions to the terminal. After delivery is complete, the user sends feedback to the server via the terminal, which is used to optimize future routes.

[0251] In this way, the system of the present invention enables efficient logistics operations even under time constraints imposed by legal revisions.

[0252] The following describes the processing flow.

[0253] Step 1:

[0254] The server collects real-time data, including location information, from the GPS devices of transport vehicles. It also obtains road congestion information from a traffic information API and the latest weather information from a weather information API. This data is stored in the server's database and used for later analysis.

[0255] Step 2:

[0256] The server analyzes the accumulated data using a generating AI to evaluate the current conditions of each transport vehicle. Here, the optimal transport route is calculated, taking into account the shortest route to the destination, expected traffic conditions, and weather conditions. A route optimization algorithm is used to generate and evaluate multiple route options.

[0257] Step 3:

[0258] The server selects the most efficient route and transmits this information to the vehicle's terminal. This information includes details of the route, estimated arrival time, and points requiring special attention.

[0259] Step 4:

[0260] The terminal displays the received route information to the driver. The driver can check the details of the current route through visual map displays and voice guidance.

[0261] Step 5:

[0262] If a user (driver) encounters an unexpected situation (such as traffic congestion or a road closure) while driving, they can request a new route from the server via their device. This request is made through a chatbot function.

[0263] Step 6:

[0264] The server re-evaluates the data in real time upon request from the driver and recalculates the transport route as needed. Once the new optimal route is determined, it is sent to the terminal.

[0265] Step 7:

[0266] The user (driver) receives the newly provided route guidance and resumes driving based on that information.

[0267] Step 8:

[0268] After delivery is complete, the user sends feedback to the server via their device regarding the actual arrival time and delivery route. This information is used to improve the accuracy of route optimization for future deliveries.

[0269] (Example 1)

[0270] 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."

[0271] There is a need to support drivers in executing deliveries along the optimal route while responding to real-time changes in traffic and weather conditions in transportation operations. However, conventional systems have problems such as difficulty in flexibly responding to sudden changes in traffic information and weather, and the inability to immediately recalculate based on driver input. This leads to challenges such as decreased transportation efficiency and delays.

[0272] 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.

[0273] In this invention, the server includes processing means for analyzing location data received from transport equipment and traffic and air quality information obtained from external information sources, and deriving the optimal transport route based on generative AI technology; communication means for transmitting the derived transport route information to the operator of the transport equipment; and dialogue means for receiving requests from the operator and recalculating and revising the transport route according to the situation. This enables efficient and flexible operation of transport routes that take into account environmental information that changes in real time.

[0274] "Transportation equipment" refers to vehicles and devices used for the purpose of transporting goods, and this includes trucks, buses, and the like.

[0275] "Location data" refers to the current geographical location information of a transportation device, usually obtained via a GPS device.

[0276] "External information source" refers to third-party services or databases that provide traffic information and atmospheric conditions, often accessed through an API.

[0277] "Traffic situation" refers to information indicating the congestion status and presence of traffic jams on roads, which is a factor affecting the movement of transportation devices.

[0278] "Atmospheric situation" refers to information related to weather and meteorology, including factors that affect the safety and efficiency of transportation.

[0279] "Generative AI technology" refers to technology that uses artificial intelligence to analyze large amounts of data and generate information suitable for specific purposes.

[0280] "Transportation route" refers to the path or route used when a transportation device moves from a specific point to a destination.

[0281] "Processing means" refers to the functions or devices for analyzing received data and performing necessary calculations.

[0282] "Communication means" refers to the technologies or devices for transmitting and receiving information, including wireless communication and network communication.

[0283] "Dialogue means" refers to devices or systems with the function of receiving input from users and performing responses or operations based on that information.

[0284] "Information storage means" refers to the technologies or devices for storing the collected data and retaining it for later analysis and use.

[0285] The present invention is a system that processes various information including the location data of a transportation device in real time and calculates an optimal transportation route. This system is mainly composed of three elements: a server, a terminal, and a user.

[0286] The server first receives location data from the GPS device installed in the transportation equipment. It also obtains data on the latest traffic conditions and atmospheric conditions from traffic APIs and weather APIs that serve as external information sources. These data are analyzed using generative AI technology, and an optimal transportation route considering traffic information and atmospheric conditions is calculated. At this time, the server uses a prompt sentence such as "Please propose the shortest route considering traffic and weather based on the current location and destination" for the generative AI model. The optimized transportation route is transmitted to the terminal using communication technology.

[0287] The terminal receives the transportation route information transmitted from the server and presents it to the driver, who is the user, through a visual map display or voice guidance. The terminal also has a chatbot function as an interaction means to enable interaction with the driver. When the driver encounters traffic congestion or road closures, the terminal can request the server to calculate an alternative route again.

[0288] The driver, who is the user, conducts transportation according to the route displayed on the terminal. After the transportation is completed, arrival information and feedback on obstacles encountered on the route are transmitted to the server through the terminal. This feedback is collected by the information accumulation means and utilized for optimizing future transportation routes.

[0289] As a specific example, consider the situation where a driver makes deliveries to multiple locations within a wide area on a certain day. In this case, the server analyzes the traffic and atmospheric conditions at each location and generates an optimal route. If the weather suddenly changes during the journey, the driver can receive a proposal for a new route through the terminal and continue the delivery. By doing so, this system realizes efficient transportation and solves the problems in the transportation business.

[0290] The flow of the specific process in Example 1 will be described using FIG. 11.

[0291] Step 1:

[0292] The server receives current location data from GPS devices installed on the transport equipment. The input is raw location information obtained from the GPS device, which the server receives and processes. The server formats this location data and converts it into a standard format for use in the next step. The output is location data in a parseable format.

[0293] Step 2:

[0294] The server obtains the latest traffic and weather data via external information sources such as traffic and weather APIs. Input is the response from the APIs, typically provided in data formats such as JSON. The server receives this data in real time, extracts the necessary information, and formats it for analysis. The output is situational information combining traffic and weather data.

[0295] Step 3:

[0296] The server uses generative AI technology to analyze location data and contextual information. Standardized location data and organized contextual information are provided to the generative AI model as input. The prompt used is "Based on the current location and destination, suggest the shortest route considering traffic and weather." The output is the optimal transport route calculated by the optimization algorithm.

[0297] Step 4:

[0298] The server transmits the calculated optimal transportation route information to the terminal. This communication utilizes a secure protocol to maintain the consistency and security of the transmitted data. The input is the calculated route information, and the output is the optimal route information received by the terminal.

[0299] Step 5:

[0300] The terminal presents the received transportation route information to the user. The input is the route information received from the server, and the output is a visual map display and voice guidance. The terminal utilizes the chatbot function and also accepts additional requests and questions input by the user. Thereby, the terminal provides efficient and safe transportation guidance to the user.

[0301] Step 6:

[0302] When the user faces a new situation during transportation, such as traffic congestion or sudden weather changes, the terminal is used to request the server to calculate an alternative route. The input is the request information from the user, and the output is the newly calculated transportation route information. The server quickly calculates a route suitable for the situation using the regeneration AI model again and transmits it to the terminal.

[0303] Step 7:

[0304] When the user completes the transportation task, the terminal transmits feedback to the server along with a completion notice. The input is the arrival information at the end of the task and the feedback regarding the problems encountered. The server receives this and accumulates the information for improving the next route calculation. The output is the improvement information stored in the database.

[0305] (Application Example 1)

[0306] 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".

[0307] In the food delivery industry, issues include delays in delivery due to traffic conditions and weather changes, resulting in a decline in customer satisfaction. Furthermore, it is difficult to select an efficient delivery route, and the burden on delivery staff is also a problem. There is a need for a system to solve these problems and achieve fast and efficient delivery.

[0308] 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.

[0309] In this invention, the server includes a computing means that acquires location data, traffic data, and weather data of a means of transport in real time, analyzes this data to generate an efficient transport route, an information transmission means that provides the generated transport route information to a person operating the means of transport, and an interaction means that receives requests from the operator and recalculates and updates the transport route as necessary. This makes it possible to deliver via the optimal route at all times.

[0310] "Real-time" refers to a timeframe in which information is processed and presented instantaneously.

[0311] "Transportation means" refers to any device or system used to move goods or people from one point to another.

[0312] "Location data" refers to information that indicates the current geographical coordinates of a specific object or person.

[0313] "Traffic data" refers to information about the flow of vehicles on roads, including conditions such as congestion and accidents.

[0314] "Weather data" refers to information about weather conditions, including temperature, precipitation, and wind speed.

[0315] "Analysis" is a detailed investigation that breaks down data and helps understand its patterns and trends.

[0316] An "efficient transport route" is a route designed to reach the destination safely and quickly in the shortest possible time.

[0317] "Computational means" refers to a device or program that has the ability to process data for a specific purpose.

[0318] "Information transmission means" refers to a process or system for effectively transmitting data or information to a person or device.

[0319] "Means of interaction" refer to the mechanisms or programs that enable humans and systems to communicate.

[0320] "Optimization" is the process of using resources in the most efficient way to achieve a goal.

[0321] The system for implementing the present invention has the ability to acquire location data, traffic data, and weather data of a means of transport in real time, analyze them, and generate the most efficient transport route. The server acquires location data from GPS devices installed on the means of transport and collects traffic and weather data using external data services. Services such as the Google Maps API and OpenWeather API are used for this purpose. This data is analyzed and optimized using a custom algorithm written in Python. The generated transport route information is transmitted to the user's terminal operating the means of transport via Wi-Fi or a cellular network.

[0322] The terminal provides this information to the user through visual map displays and voice guidance. Users can also ask questions and make requests in real time using the terminal's built-in chatbot function. This interaction allows users to quickly respond to problems or changes that occur during transport. After delivery is complete, the terminal sends feedback from the user and recipient to a server, and this information is used to optimize future transport routes.

[0323] As a concrete example, in food delivery, there is a need for the automatic generation of efficient routes during peak hours. The server reflects traffic congestion and weather changes in real time, helping delivery personnel deliver goods to multiple destinations within a set time. In this case, the food delivery application presents the user with the latest route and notifies them immediately if there are any changes.

[0324] As an example of a specific prompt to the generating AI model, the following is used: "For the concentrated orders in the Shinjuku area, calculate an efficient delivery route based on current traffic conditions and weather data. Provide the optimal route for the next 30 minutes and notify the delivery driver by voice."

[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 location data from GPS devices installed on the means of transport. The input is geographic coordinates (latitude and longitude) provided by the GPS device. The server extracts this data and stores it temporarily. The output is the current, accurate location data, which is then recognized by the server.

[0328] Step 2:

[0329] The server uses external API services (e.g., Google Maps API, OpenWeather API) to retrieve current and predicted traffic and weather data. The input consists of queries to the APIs, including location data as a parameter. The server analyzes the data retrieved from these APIs to identify traffic congestion and weather conditions. The output provides traffic and weather conditions for a specific area.

[0330] Step 3:

[0331] The server uses a generative AI model to calculate the optimal transport route based on acquired location data, traffic data, and weather data. The input is the dataset obtained in the previous steps. The generative AI model processes this data and calculates the route that maximizes efficiency. The output is the optimized transport route.

[0332] Step 4:

[0333] The server sends the generated optimal transport route to the user's terminal operating the transport. As input, route information is formatted and transmitted via communication. The terminal receives this information and prepares to provide visual and audio guidance to the user. As output, real-time route guidance data is displayed on the terminal.

[0334] Step 5:

[0335] The user performs transportation based on information provided via the terminal. They may also use the chatbot function to request a route recalculation if necessary. Input includes user feedback and additional instructions. The terminal processes these requests and sends new requests to the server. The output is the updated transportation route, if necessary, displayed again on the terminal.

[0336] Step 6:

[0337] Once delivery is complete, the user enters a delivery completion report into the terminal. This input includes information such as confirmation of successful delivery and any problems encountered during transit. The terminal collects this feedback and sends it back to the server. As output, the server accumulates feedback information that will be used for future optimizations.

[0338] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0339] In embodiments of the present invention, a system is provided that not only determines the efficient route of transport vehicles in real time, but also analyzes the user's emotions and adapts the system's operation accordingly. The system mainly consists of a server, a terminal, a user, and an emotion engine.

[0340] The server is responsible for collecting location, traffic, and weather information related to transport vehicles. This data is analyzed by a generative AI and forms the basis for calculating the optimal transport route. The calculated optimal route information is then transmitted to the terminals of each transport vehicle for use.

[0341] The device has a built-in chatbot function for interacting with the driver (user). Using this function, users can request new routes as needed while driving. Furthermore, the device incorporates an emotion engine that can understand the user's emotional state in real time through speech recognition and text analysis.

[0342] The emotion engine recognizes the user's emotions, which are then sent to the server and used to adjust the optimal route and select the appropriate communication method as needed. For example, if the system determines that the user is feeling stressed or anxious, it can select and provide a calmer voice guidance.

[0343] For example, if a user reports fatigue due to long hours of driving, the system detects this emotion through its emotion engine. The server then prioritizes guiding the user to an immediately available rest area and provides this information to the terminal. If the user is relaxed, the system continues with normal guidance.

[0344] After delivery is complete, feedback, including user emotional data, is stored on the server. This information is used to improve the accuracy of future optimization algorithms, enabling more effective support for operations. As a result, the system of this invention achieves efficient logistics while reducing the psychological stress on drivers.

[0345] The following describes the processing flow.

[0346] Step 1:

[0347] The server collects location information from GPS devices installed in transport vehicles. It also obtains the latest road and weather information via traffic and weather APIs. This data is integrated on the server and used for real-time route optimization.

[0348] Step 2:

[0349] The server analyzes the collected data using generating AI to evaluate the operating conditions of each transport vehicle. This includes a process of selecting the optimal transport route, taking into account the shortest route to the destination, congestion levels, and weather conditions.

[0350] Step 3:

[0351] The server transmits the generated optimal route information to the terminal of each transport vehicle. This information includes specific route details, estimated travel time, and important notes.

[0352] Step 4:

[0353] The terminal receives route information from the server and presents it to the user through a visual map display and voice guidance. The terminal also incorporates an emotion engine that analyzes the user's emotional state while driving based on their input.

[0354] Step 5:

[0355] If a user reports stress or fatigue while driving, the device uses an emotion engine to detect that emotion and sends feedback to the server.

[0356] Step 6:

[0357] The server analyzes the user's emotional state received from the emotion engine and adjusts the route and guidance messages as needed. For example, it might guide the user to a relaxing resting place or switch to a calmer voice guidance.

[0358] Step 7:

[0359] When a user requests a new route, the device sends the request to the server. The server immediately recalculates the route and provides the device with the new directions.

[0360] Step 8:

[0361] After delivery is complete, the user sends arrival time, delivery route, and emotional feedback to the server via their device. This information is stored in the server's database and used to optimize future delivery 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] In modern times, while there is a demand for increased efficiency in transport vehicles, there is a challenge in that operational support that takes into account the psychological state of drivers is not adequately provided. Therefore, there is a need to develop systems that are efficient while reducing driver stress.

[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] This invention includes a server that includes a processing means for acquiring location information, traffic information, and weather information related to transport vehicles in real time and analyzing these multiple pieces of information to generate an optimal transport route; an adaptive means for analyzing the driver's emotions and adaptively changing voice guidance and route guidance based on that; and a data storage means for collecting feedback, including the driver's emotional data, after delivery is completed and utilizing it for optimizing the next transport route. This makes efficient transport possible while reducing the psychological burden on the driver.

[0367] "Real-time" refers to the instantaneous acquisition or processing of information and data without delay.

[0368] A "transport vehicle" is a machine or device used to transport goods or passengers, and generally includes those that primarily travel on roads or railways.

[0369] "Location information" refers to data that indicates the geographical location of a specific object or person, and is usually represented by latitude and longitude.

[0370] "Traffic information" refers to data on vehicle flow, traffic congestion, and road conditions.

[0371] "Weather information" refers to data related to meteorological conditions, including information such as temperature, precipitation, and wind speed.

[0372] "Processing means" refers to a method or apparatus for receiving, processing, analyzing, and interpreting data.

[0373] A "generative AI model" refers to a framework of artificial intelligence that performs pattern recognition and information generation based on large amounts of data.

[0374] A "prompt message" refers to an input sentence used to give instructions or questions to an AI model.

[0375] "Communication means" refers to methods and devices for sending and receiving information.

[0376] "Dialogue means" refers to methods and devices for users and systems to exchange information and instructions.

[0377] "Emotional analysis" refers to estimating a person's psychological state based on information such as audio and text.

[0378] "Adaptive measures" refer to methods or devices for changing or adapting systems or processes according to the situation.

[0379] "Data storage means" refers to methods and devices for storing collected information and data for later use.

[0380] This invention is a system in which a server, terminal, and user interact with each other to improve the efficiency of transport vehicle operations in real time, and to provide support tailored to the driver's psychological state.

[0381] The server first collects necessary data using the vehicle's location information, traffic information, and weather information. GPS devices and various APIs are used for this data collection. For example, traffic information is obtained using the Google Maps API, and weather data is acquired using weather information APIs. Next, the server analyzes this data using a generative AI model to generate the optimal transport route. The generated route information is then transmitted to the driver's terminal via communication means.

[0382] The terminal is a central element supporting interaction with the driver. Chatbot functionality allows drivers to interact with the terminal via voice or text, obtaining information during their journey and issuing instructions for route changes. Furthermore, a built-in emotion analysis engine analyzes the driver's voice and text to estimate their psychological state. For example, it uses a speech recognition API to analyze the driver's tone and detect signs of stress or fatigue.

[0383] If a user reports feeling fatigued due to prolonged driving, the server uses emotional information and a generative AI model to generate a prompt such as, "Guide me to the best route from my current location to the nearest rest stop." Based on this prompt, the driver is provided with appropriate route guidance. If the user is relaxed, the system continues with normal guidance.

[0384] As a result, this system can reduce the psychological burden on drivers and enable efficient transportation.

[0385] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0386] Step 1:

[0387] The server collects location information of transport vehicles obtained from GPS devices, traffic information from traffic APIs, and weather information from weather APIs. This information is input to the server as raw data and stored in a database. This stored data forms the basis for subsequent analysis and route calculations.

[0388] Step 2:

[0389] The server inputs the collected data into a generating AI model to calculate the optimal transport route. Specifically, it determines the main route from the current vehicle's location to its destination based on location and traffic information, and then adapts that route considering weather conditions. The outputted optimal route information becomes the data that will be transmitted to the driver in the next step.

[0390] Step 3:

[0391] The server sends the generated optimal route information to the driver's terminal. This uses a mechanism that updates the data in real time using a communication protocol. The route information received by the terminal is displayed for the user's reference.

[0392] Step 4:

[0393] The terminal initiates a conversation with the driver, using a chatbot function to accept voice commands and text input from the driver. If the driver requests a new route, the terminal resends the request to the server to prompt a route recalculation. The input at this time is a new route request, and the output is the new route information.

[0394] Step 5:

[0395] The device activates an emotion engine and analyzes voice and text from the driver. This analysis is performed to determine the driver's stress and fatigue levels, and emotional data is output. This helps to understand the driver's psychological state and assists in taking the next steps.

[0396] Step 6:

[0397] The server receives emotion data sent from the terminal and communicates it to the AI ​​model that generates new prompts, thereby formulating appropriate countermeasures. For example, a prompt such as "Provide calm navigation" can be output by the system, enabling it to provide guidance tailored to the driver's psychological state.

[0398] Step 7:

[0399] After delivery is complete, user feedback is sent from the terminal to the server. This feedback includes driver sentiment data and is used as a data accumulation tool. The accumulated data is used to optimize future routes and improve the system.

[0400] (Application Example 2)

[0401] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0402] Optimizing both operational efficiency and worker stress simultaneously in a logistics center is not easy. In particular, proceeding with work while ignoring the emotional state of individual workers can lead to decreased efficiency and increased errors. Therefore, it is necessary to understand workers' emotional states in real time and provide work instructions accordingly.

[0403] 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.

[0404] In this invention, the server includes information processing means that acquire location information and traffic information of transport devices in real time and analyze this data to generate an optimal transport route; emotion recognition means that identify the emotional state based on the user's voice and facial expression data and adjust the guidance method accordingly; and communication means that provide the generated transport route information to the user. This makes it possible to provide optimal work instructions in a logistics center according to the emotional state of the workers.

[0405] "Real-time" is a concept that refers to information and actions being processed immediately and used without delay when needed.

[0406] "Transportation equipment" refers to a machine or system used to transport goods or people to a designated location.

[0407] "Location information" refers to data that indicates the geographical coordinates or location of a specific object.

[0408] "Traffic information" refers to data on road and public transportation congestion and operating conditions.

[0409] "Information processing means" refers to devices and programs used to analyze data and extract and process necessary information.

[0410] "Voice and facial expression data" refers to digital information about the characteristics of the user's voice and facial expressions.

[0411] "Emotional state" refers to the psychological or emotional state that an individual is currently experiencing.

[0412] "Emotion recognition means" refers to technologies and devices that analyze and identify an individual's emotional state based on data such as voice and facial expressions.

[0413] "Communication means" refers to the technology or equipment used to send and receive information.

[0414] "Information storage means" refers to a device or system for securely storing data and information for later use.

[0415] An example of this invention's application is a system designed to improve work efficiency and reduce worker stress in a logistics center. This system aims to provide workers with real-time information using smart glasses.

[0416] The server collects inventory data, shelf location information, and worker location information within the logistics center. This data is analyzed by a generative AI model to generate the optimal picking route. The server also combines this data with environmental data to determine the most efficient work procedure. The specific software used includes OpenAI as the generative AI model and Amazon Rekognition as the emotion recognition engine.

[0417] The device refers to smart glasses that visualize the generated picking route for the worker and provide voice guidance. The glasses' built-in camera and microphone collect the worker's voice and facial expression data, and analyze their emotional state in real time. Based on their emotions, the system adjusts the work route and suggests breaks to reduce workload.

[0418] For example, when a staff member at a logistics center is picking complex items, if the system detects that the worker is fatigued, the server will immediately adjust the work route and suggest a break. An example of a prompt message would be, "Based on staff emotional data, please suggest the optimal method for adjusting the workload."

[0419] This system makes it possible for workers within the logistics center to perform their tasks efficiently and effectively.

[0420] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0421] Step 1:

[0422] The server collects inventory, shelf locations, and worker location information within the logistics center. Using this data as input, a generative AI model calculates the optimal picking route. As output, customized route information is generated for each worker.

[0423] In terms of specific operations, the process involves querying a database to retrieve the necessary data, then passing that data to a generative AI model for analysis.

[0424] Step 2:

[0425] The smart glasses, acting as the terminal, display the picking route received from the server in the worker's field of vision and also provide voice guidance. The input for this process is route information from the server, and the output is the presentation of information to the worker.

[0426] The specific actions include displaying graphics on a display built into the smart glasses and playing audio from the speaker.

[0427] Step 3:

[0428] The user's voice and facial expressions are captured by the terminal's camera and microphone. This data is used as input by an emotion recognition engine, which analyzes it to identify the worker's emotional state. The output is digital data related to the current emotional state.

[0429] Specifically, the collected audio and video data is sent to an emotion recognition engine in the cloud for analysis.

[0430] Step 4:

[0431] The server receives the worker's emotional state from the emotion recognition engine and uses this information to optimize picking routes and work instructions. The input for this step is emotional state data, and the output is the adjusted work instructions.

[0432] The specific actions include route optimization using algorithms based on sentiment data and the generation of new instruction data.

[0433] Step 5:

[0434] When a user completes a task, they send their feedback to the server. This generates accumulated data that is used to optimize the next picking route. The input is the worker's feedback, and the output is an update of the data used for future optimization.

[0435] The specific actions include displaying a short questionnaire on smart glasses after the work is completed, and sending the responses to a server for storage in a database.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] [Third Embodiment]

[0440] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0441] 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.

[0442] 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).

[0443] 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.

[0444] 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.

[0445] 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).

[0446] 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.

[0447] 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.

[0448] 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.

[0449] 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.

[0450] 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.

[0451] 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".

[0452] In an embodiment of the present invention, a system is provided for determining the efficient route of transport vehicles in real time. The system mainly consists of a server, a terminal, and a user.

[0453] The server receives location information from GPS devices installed in the transport vehicles. It also obtains the latest traffic and weather conditions from external traffic and weather information APIs. This data is analyzed by a generated AI on the server, and the optimal route for each transport vehicle is calculated based on an optimization algorithm. The generated optimal route is then transmitted to the vehicle's terminal.

[0454] The terminal receives optimal route information transmitted from the server and presents it to the driver (user). This is done through visual map displays and voice guidance. The terminal is equipped with a chatbot function as a means of interaction, and can receive questions from the driver. For example, if the driver encounters traffic congestion or unexpected road closures, they can request alternative route suggestions through the terminal.

[0455] Technically, the server can recalculate the route as needed and send the updated route to the terminal. This two-way communication ensures that drivers always follow the optimal route for deliveries. Once a delivery is complete, the terminal sends a delivery completion notification to the server, which includes feedback on the actual arrival time and any problems encountered along the route.

[0456] As a concrete example, consider a scenario where a user has multiple delivery destinations within the Tohoku region on a given day. The server analyzes traffic conditions and weather data for each destination and generates the shortest route. If an unexpected weather change is reported along the way, the server calculates an alternative route in response to the user's request and provides new directions to the terminal. After delivery is complete, the user sends feedback to the server via the terminal, which is used to optimize future routes.

[0457] In this way, the system of the present invention enables efficient logistics operations even under time constraints imposed by legal revisions.

[0458] The following describes the processing flow.

[0459] Step 1:

[0460] The server collects real-time data, including location information, from the GPS devices of transport vehicles. It also obtains road congestion information from a traffic information API and the latest weather information from a weather information API. This data is stored in the server's database and used for later analysis.

[0461] Step 2:

[0462] The server analyzes the accumulated data using a generating AI to evaluate the current conditions of each transport vehicle. Here, the optimal transport route is calculated, taking into account the shortest route to the destination, expected traffic conditions, and weather conditions. A route optimization algorithm is used to generate and evaluate multiple route options.

[0463] Step 3:

[0464] The server selects the most efficient route and transmits this information to the vehicle's terminal. This information includes details of the route, estimated arrival time, and points requiring special attention.

[0465] Step 4:

[0466] The terminal displays the received route information to the driver. The driver can check the details of the current route through visual map displays and voice guidance.

[0467] Step 5:

[0468] If a user (driver) encounters an unexpected situation (such as traffic congestion or a road closure) while driving, they can request a new route from the server via their device. This request is made through a chatbot function.

[0469] Step 6:

[0470] The server re-evaluates the data in real time upon request from the driver and recalculates the transport route as needed. Once the new optimal route is determined, it is sent to the terminal.

[0471] Step 7:

[0472] The user (driver) receives the newly provided route guidance and resumes driving based on that information.

[0473] Step 8:

[0474] After delivery is complete, the user sends feedback to the server via their device regarding the actual arrival time and delivery route. This information is used to improve the accuracy of route optimization for future deliveries.

[0475] (Example 1)

[0476] 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."

[0477] There is a need to support drivers in executing deliveries along the optimal route while responding to real-time changes in traffic and weather conditions in transportation operations. However, conventional systems have problems such as difficulty in flexibly responding to sudden changes in traffic information and weather, and the inability to immediately recalculate based on driver input. This leads to challenges such as decreased transportation efficiency and delays.

[0478] 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.

[0479] In this invention, the server includes processing means for analyzing location data received from transport equipment and traffic and air quality information obtained from external information sources, and deriving the optimal transport route based on generative AI technology; communication means for transmitting the derived transport route information to the operator of the transport equipment; and dialogue means for receiving requests from the operator and recalculating and revising the transport route according to the situation. This enables efficient and flexible operation of transport routes that take into account environmental information that changes in real time.

[0480] "Transportation equipment" refers to vehicles and devices used for the purpose of transporting goods, and this includes trucks, buses, and the like.

[0481] "Location data" refers to the current geographical location information of a transport device, which is usually obtained via a GPS device.

[0482] "External information sources" refer to third-party services and databases that provide traffic information and air quality data, and are often accessed via APIs.

[0483] "Traffic conditions" refer to information indicating the degree of road congestion and the presence or absence of traffic jams, and are factors that affect the movement of transportation equipment.

[0484] "Atmospheric conditions" refers to information about weather and climate, including factors that affect the safety and efficiency of transportation.

[0485] "Generative AI technology" refers to technology that uses artificial intelligence to analyze large amounts of data and generate information suitable for a specific purpose.

[0486] A "transportation route" refers to the path or route used by transportation equipment to move from a specific point to its destination.

[0487] "Processing means" refers to functions or devices for analyzing received data and performing necessary calculations.

[0488] "Communication methods" refer to technologies and devices for sending and receiving information, and this includes wireless communication and network communication.

[0489] "Dialogue means" refers to devices or systems that have the function of receiving input from users and responding or performing operations based on that information.

[0490] "Information storage means" refers to technologies and devices that store collected data and retain it for later analysis and use.

[0491] This invention relates to a system that processes various types of information, including location data of transportation equipment, in real time and calculates the optimal transportation route. This system mainly consists of three elements: a server, a terminal, and a user.

[0492] The server first receives location data from GPS devices installed in the transport equipment. It also obtains the latest traffic and weather data from external information sources such as traffic APIs and weather APIs. This data is analyzed using generative AI technology to calculate the optimal transport route, taking traffic information and weather conditions into account. During this process, the server prompts the generative AI model with the message, "Based on the current location and destination, please suggest the shortest route considering traffic and weather." The optimized transport route is then transmitted to the terminal using communication technology.

[0493] The terminal receives transportation route information transmitted from the server and presents it to the user (driver) through visual map displays and voice guidance. The terminal also features a chatbot function as a means of interaction with the driver. If the driver encounters traffic congestion or road closures, the terminal can request the server to calculate alternative routes again.

[0494] The user, acting as the driver, transports the goods according to the route displayed on the terminal. After completing the transport, the driver sends arrival information and feedback about any obstacles encountered along the route to the server via the terminal. This feedback is collected by an information storage system and used to optimize future transport routes.

[0495] As a concrete example, consider a scenario where a driver makes deliveries to multiple locations within a wide area on a given day. In this case, the server analyzes traffic and weather conditions at each location and generates the optimal route. If the weather suddenly changes along the way, the driver can receive a suggestion for a new route via a terminal and continue deliveries. In this way, the system achieves efficient transportation and solves challenges in transportation operations.

[0496] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0497] Step 1:

[0498] The server receives current location data from GPS devices installed on the transport equipment. The input is raw location information obtained from the GPS device, which the server receives and processes. The server formats this location data and converts it into a standard format for use in the next step. The output is location data in a parseable format.

[0499] Step 2:

[0500] The server obtains the latest traffic and weather data via external information sources such as traffic and weather APIs. Input is the response from the APIs, typically provided in data formats such as JSON. The server receives this data in real time, extracts the necessary information, and formats it for analysis. The output is situational information combining traffic and weather data.

[0501] Step 3:

[0502] The server uses generative AI technology to analyze location data and contextual information. Standardized location data and organized contextual information are provided to the generative AI model as input. The prompt used is "Based on the current location and destination, suggest the shortest route considering traffic and weather." The output is the optimal transport route calculated by the optimization algorithm.

[0503] Step 4:

[0504] The server transmits the calculated optimal transportation route information to the terminal. This communication utilizes a secure protocol to maintain the consistency and security of the transmitted data. The input is the calculated route information, and the output is the optimal route information received by the terminal.

[0505] Step 5:

[0506] The terminal displays received transportation route information to the user. Input is route information received from the server, and output is a visual map display and voice guidance. The terminal utilizes chatbot functionality to accept additional requests and questions from the user. This allows the terminal to provide users with efficient and safe transportation guidance.

[0507] Step 6:

[0508] If a user encounters a new situation during transport, such as traffic congestion or a sudden change in weather, they can use their terminal to request the server to calculate an alternative route. The input is the user's request information, and the output is the newly calculated transport route information. The server then uses the regenerated AI model to quickly calculate a route suitable for the situation and sends it to the terminal.

[0509] Step 7:

[0510] When a user completes a transportation task, they send a completion notification and feedback from their terminal to the server. The input consists of arrival information upon completion of the task and feedback on any problems encountered. The server receives this information and uses it to improve future route calculations. The output is the improvement information stored in the database.

[0511] (Application Example 1)

[0512] 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."

[0513] In the food delivery industry, delays due to traffic conditions and weather changes are a major challenge, leading to decreased customer satisfaction. Furthermore, selecting efficient delivery routes is difficult, placing a heavy burden on delivery personnel. A system is needed to address these challenges and achieve fast and efficient delivery.

[0514] 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.

[0515] In this invention, the server includes a computing means that acquires location data, traffic data, and weather data of a means of transport in real time, analyzes this data to generate an efficient transport route, an information transmission means that provides the generated transport route information to a person operating the means of transport, and an interaction means that receives requests from the operator and recalculates and updates the transport route as necessary. This makes it possible to deliver via the optimal route at all times.

[0516] "Real-time" refers to a timeframe in which information is processed and presented instantaneously.

[0517] "Transportation means" refers to any device or system used to move goods or people from one point to another.

[0518] "Location data" refers to information that indicates the current geographical coordinates of a specific object or person.

[0519] "Traffic data" refers to information about the flow of vehicles on roads, including conditions such as congestion and accidents.

[0520] "Weather data" refers to information about weather conditions, including temperature, precipitation, and wind speed.

[0521] "Analysis" is a detailed investigation that breaks down data and helps understand its patterns and trends.

[0522] An "efficient transport route" is a route designed to reach the destination safely and quickly in the shortest possible time.

[0523] "Computational means" refers to a device or program that has the ability to process data for a specific purpose.

[0524] "Information transmission means" refers to a process or system for effectively transmitting data or information to a person or device.

[0525] "Means of interaction" refer to the mechanisms or programs that enable humans and systems to communicate.

[0526] "Optimization" is the process of using resources in the most efficient way to achieve a goal.

[0527] The system for implementing the present invention has the ability to acquire location data, traffic data, and weather data of a means of transport in real time, analyze them, and generate the most efficient transport route. The server acquires location data from GPS devices installed on the means of transport and collects traffic and weather data using external data services. Services such as the Google Maps API and OpenWeather API are used for this purpose. This data is analyzed and optimized using a custom algorithm written in Python. The generated transport route information is transmitted to the user's terminal operating the means of transport via Wi-Fi or a cellular network.

[0528] The terminal provides this information to the user through visual map displays and voice guidance. Users can also ask questions and make requests in real time using the terminal's built-in chatbot function. This interaction allows users to quickly respond to problems or changes that occur during transport. After delivery is complete, the terminal sends feedback from the user and recipient to a server, and this information is used to optimize future transport routes.

[0529] As a concrete example, in food delivery, there is a need for the automatic generation of efficient routes during peak hours. The server reflects traffic congestion and weather changes in real time, helping delivery personnel deliver goods to multiple destinations within a set time. In this case, the food delivery application presents the user with the latest route and notifies them immediately if there are any changes.

[0530] As an example of a specific prompt for the generating AI model, the following is used: "For the concentrated orders in the Shinjuku area, calculate an efficient delivery route based on current traffic conditions and weather data. Provide the optimal route for the next 30 minutes and notify the delivery driver by voice."

[0531] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0532] Step 1:

[0533] The server receives real-time location data from GPS devices installed on the means of transport. The input is geographic coordinates (latitude and longitude) provided by the GPS device. The server extracts this data and stores it temporarily. The output is the current, accurate location data, which is then recognized by the server.

[0534] Step 2:

[0535] The server uses external API services (e.g., Google Maps API, OpenWeather API) to retrieve current and predicted traffic and weather data. The input consists of queries to the APIs, including location data as a parameter. The server analyzes the data retrieved from these APIs to identify traffic congestion and weather conditions. The output provides traffic and weather conditions for a specific area.

[0536] Step 3:

[0537] The server uses a generative AI model to calculate the optimal transport route based on acquired location data, traffic data, and weather data. The input is the dataset obtained in the previous steps. The generative AI model processes this data and calculates the route that maximizes efficiency. The output is the optimized transport route.

[0538] Step 4:

[0539] The server sends the generated optimal transport route to the user's terminal operating the transport. As input, route information is formatted and transmitted via communication. The terminal receives this information and prepares to provide visual and audio guidance to the user. As output, real-time route guidance data is displayed on the terminal.

[0540] Step 5:

[0541] The user performs transportation based on information provided via the terminal. They may also use the chatbot function to request a route recalculation if necessary. Input includes user feedback and additional instructions. The terminal processes these requests and sends new requests to the server. The output is the updated transportation route, if necessary, displayed again on the terminal.

[0542] Step 6:

[0543] Once delivery is complete, the user enters a delivery completion report into the terminal. This input includes information such as confirmation of successful delivery and any problems encountered during transit. The terminal collects this feedback and sends it back to the server. As output, the server accumulates feedback information that will be used for future optimizations.

[0544] 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.

[0545] In embodiments of the present invention, a system is provided that not only determines the efficient route of transport vehicles in real time, but also analyzes the user's emotions and adapts the system's operation accordingly. The system mainly consists of a server, terminals, users, and an emotion engine.

[0546] The server is responsible for collecting location, traffic, and weather information related to transport vehicles. This data is analyzed by a generative AI and forms the basis for calculating the optimal transport route. The calculated optimal route information is then transmitted to the terminals of each transport vehicle for use.

[0547] The device has a built-in chatbot function for interacting with the driver (user). Using this function, users can request new routes as needed while driving. Furthermore, the device incorporates an emotion engine that can understand the user's emotional state in real time through speech recognition and text analysis.

[0548] The emotion engine recognizes the user's emotions, which are then sent to the server and used to adjust the optimal route and select the appropriate communication method as needed. For example, if the system determines that the user is feeling stressed or anxious, it can select and provide a calmer voice guidance.

[0549] For example, if a user reports fatigue due to long hours of driving, the system detects this emotion through its emotion engine. The server then prioritizes guiding the user to an immediately available rest area and provides this information to the terminal. If the user is relaxed, the system continues with normal guidance.

[0550] After delivery is complete, feedback, including user emotional data, is stored on the server. This information is used to improve the accuracy of future optimization algorithms, enabling more effective support for operations. As a result, the system of this invention achieves efficient logistics while reducing the psychological stress on drivers.

[0551] The following describes the processing flow.

[0552] Step 1:

[0553] The server collects location information from GPS devices installed in transport vehicles. It also obtains the latest road and weather information via traffic and weather APIs. This data is integrated on the server and used for real-time route optimization.

[0554] Step 2:

[0555] The server analyzes the collected data using generating AI to evaluate the operating conditions of each transport vehicle. This includes a process of selecting the optimal transport route, taking into account the shortest route to the destination, congestion levels, and weather conditions.

[0556] Step 3:

[0557] The server transmits the generated optimal route information to the terminal of each transport vehicle. This information includes specific route details, estimated travel time, and important notes.

[0558] Step 4:

[0559] The terminal receives route information from the server and presents it to the user through a visual map display and voice guidance. The terminal also incorporates an emotion engine that analyzes the user's emotional state while driving based on their input.

[0560] Step 5:

[0561] If a user reports stress or fatigue while driving, the device uses an emotion engine to detect that emotion and sends feedback to the server.

[0562] Step 6:

[0563] The server analyzes the user's emotional state received from the emotion engine and adjusts the route and guidance messages as needed. For example, it might guide the user to a relaxing resting place or switch to a calmer voice guidance.

[0564] Step 7:

[0565] When a user requests a new route, the device sends the request to the server. The server immediately recalculates the route and provides the device with the new directions.

[0566] Step 8:

[0567] After delivery is complete, the user sends arrival time, delivery route, and emotional feedback to the server via their device. This information is stored in the server's database and used to optimize future delivery plans.

[0568] (Example 2)

[0569] 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."

[0570] In modern times, while there is a demand for increased efficiency in transport vehicles, there is a challenge in that operational support that takes into account the psychological state of drivers is not adequately provided. Therefore, there is a need to develop systems that are efficient while reducing driver stress.

[0571] 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.

[0572] This invention includes a server that includes a processing means for acquiring location information, traffic information, and weather information related to transport vehicles in real time and analyzing these multiple pieces of information to generate an optimal transport route; an adaptive means for analyzing the driver's emotions and adaptively changing voice guidance and route guidance based on that; and a data storage means for collecting feedback, including the driver's emotional data, after delivery is completed and utilizing it for optimizing the next transport route. This makes efficient transport possible while reducing the psychological burden on the driver.

[0573] "Real-time" refers to the instantaneous acquisition or processing of information and data without delay.

[0574] A "transport vehicle" is a machine or device used to transport goods or passengers, and generally includes those that primarily travel on roads or railways.

[0575] "Location information" refers to data that indicates the geographical location of a specific object or person, and is usually represented by latitude and longitude.

[0576] "Traffic information" refers to data on vehicle flow, traffic congestion, and road conditions.

[0577] "Weather information" refers to data related to meteorological conditions, including information such as temperature, precipitation, and wind speed.

[0578] "Processing means" refers to a method or apparatus for receiving, processing, analyzing, and interpreting data.

[0579] A "generative AI model" refers to a framework of artificial intelligence that performs pattern recognition and information generation based on large amounts of data.

[0580] A "prompt message" refers to an input sentence used to give instructions or questions to an AI model.

[0581] "Communication means" refers to methods and devices for sending and receiving information.

[0582] "Dialogue means" refers to methods and devices for users and systems to exchange information and instructions.

[0583] "Emotional analysis" refers to estimating a person's psychological state based on information such as audio and text.

[0584] "Adaptive measures" refer to methods or devices for changing or adapting systems or processes according to the situation.

[0585] "Data storage means" refers to methods and devices for storing collected information and data for later use.

[0586] This invention is a system in which a server, terminal, and user interact with each other to improve the efficiency of transport vehicle operations in real time, and to provide support tailored to the driver's psychological state.

[0587] The server first collects necessary data using the vehicle's location information, traffic information, and weather information. GPS devices and various APIs are used for this data collection. For example, traffic information is obtained using the Google Maps API, and weather data is acquired using weather information APIs. Next, the server analyzes this data using a generative AI model to generate the optimal transport route. The generated route information is then transmitted to the driver's terminal via communication means.

[0588] The terminal is a central element supporting interaction with the driver. Chatbot functionality allows drivers to interact with the terminal via voice or text, obtaining information during their journey and issuing instructions for route changes. Furthermore, a built-in emotion analysis engine analyzes the driver's voice and text to estimate their psychological state. For example, it uses a speech recognition API to analyze the driver's tone and detect signs of stress or fatigue.

[0589] If a user reports feeling fatigued due to prolonged driving, the server uses emotional information and a generative AI model to generate a prompt such as, "Guide me to the best route from my current location to the nearest rest stop." Based on this prompt, the driver is provided with appropriate route guidance. If the user is relaxed, the system continues with normal guidance.

[0590] As a result, this system can reduce the psychological burden on drivers and enable efficient transportation.

[0591] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0592] Step 1:

[0593] The server collects location information of transport vehicles obtained from GPS devices, traffic information from traffic APIs, and weather information from weather APIs. This information is input to the server as raw data and stored in a database. This stored data forms the basis for subsequent analysis and route calculations.

[0594] Step 2:

[0595] The server inputs the collected data into a generating AI model to calculate the optimal transport route. Specifically, it determines the main route from the current vehicle's location to its destination based on location and traffic information, and then adapts that route considering weather conditions. The outputted optimal route information becomes the data that will be transmitted to the driver in the next step.

[0596] Step 3:

[0597] The server sends the generated optimal route information to the driver's terminal. This uses a mechanism that updates the data in real time using a communication protocol. The route information received by the terminal is displayed for the user's reference.

[0598] Step 4:

[0599] The terminal initiates a conversation with the driver, using a chatbot function to accept voice commands and text input from the driver. If the driver requests a new route, the terminal resends the request to the server to prompt a route recalculation. The input at this time is a new route request, and the output is the new route information.

[0600] Step 5:

[0601] The device activates an emotion engine and analyzes voice and text from the driver. This analysis is performed to determine the driver's stress and fatigue levels, and emotional data is output. This helps to understand the driver's psychological state and assists in taking the next steps.

[0602] Step 6:

[0603] The server receives emotion data sent from the terminal and communicates it to the AI ​​model that generates new prompts, thereby formulating appropriate countermeasures. For example, a prompt such as "Provide calm navigation" can be output by the system, enabling it to provide guidance tailored to the driver's psychological state.

[0604] Step 7:

[0605] After delivery is complete, user feedback is sent from the terminal to the server. This feedback includes driver sentiment data and is used as a data accumulation tool. The accumulated data is used to optimize future routes and improve the system.

[0606] (Application Example 2)

[0607] 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."

[0608] Optimizing both operational efficiency and worker stress simultaneously in a logistics center is not easy. In particular, proceeding with work while ignoring the emotional state of individual workers can lead to decreased efficiency and increased errors. Therefore, it is necessary to understand workers' emotional states in real time and provide work instructions accordingly.

[0609] 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.

[0610] In this invention, the server includes information processing means that acquire location information and traffic information of transport devices in real time and analyze this data to generate an optimal transport route; emotion recognition means that identify the emotional state based on the user's voice and facial expression data and adjust the guidance method accordingly; and communication means that provide the generated transport route information to the user. This makes it possible to provide optimal work instructions in a logistics center according to the emotional state of the workers.

[0611] "Real-time" is a concept that refers to information and actions being processed immediately and used without delay when needed.

[0612] "Transportation equipment" refers to a machine or system used to transport goods or people to a designated location.

[0613] "Location information" refers to data that indicates the geographical coordinates or location of a specific object.

[0614] "Traffic information" refers to data on road and public transportation congestion and operating conditions.

[0615] "Information processing means" refers to devices and programs used to analyze data and extract and process necessary information.

[0616] "Voice and facial expression data" refers to digital information about the characteristics of the user's voice and facial expressions.

[0617] "Emotional state" refers to the psychological or emotional state that an individual is currently experiencing.

[0618] "Emotion recognition means" refers to technologies and devices that analyze and identify an individual's emotional state based on data such as voice and facial expressions.

[0619] "Communication means" refers to the technology or equipment used to send and receive information.

[0620] "Information storage means" refers to a device or system for securely storing data and information for later use.

[0621] An example of this invention's application is a system designed to improve work efficiency and reduce worker stress in a logistics center. This system aims to provide workers with real-time information using smart glasses.

[0622] The server collects inventory data, shelf location information, and worker location information within the logistics center. This data is analyzed by a generative AI model to generate the optimal picking route. The server also combines this data with environmental data to determine the most efficient work procedure. The specific software used includes OpenAI as the generative AI model and Amazon Rekognition as the emotion recognition engine.

[0623] The device refers to smart glasses that visualize the generated picking route for the worker and provide voice guidance. The glasses' built-in camera and microphone collect the worker's voice and facial expression data, and analyze their emotional state in real time. Based on their emotions, the system adjusts the work route and suggests breaks to reduce workload.

[0624] For example, when a staff member at a logistics center is picking complex items, if the system detects that the worker is fatigued, the server will immediately adjust the work route and suggest a break. An example of a prompt message would be, "Based on staff emotional data, please suggest the optimal method for adjusting the workload."

[0625] This system makes it possible for workers within the logistics center to perform their tasks efficiently and effectively.

[0626] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0627] Step 1:

[0628] The server collects inventory, shelf locations, and worker location information within the logistics center. Using this data as input, a generative AI model calculates the optimal picking route. As output, customized route information is generated for each worker.

[0629] In terms of specific operations, it executes queries to the database to retrieve the necessary data, and then passes that data to the generative AI model for analysis.

[0630] Step 2:

[0631] The smart glasses, acting as the terminal, display the picking route received from the server in the worker's field of vision and also provide voice guidance. The input for this process is route information from the server, and the output is the presentation of information to the worker.

[0632] The specific actions include displaying graphics on a display built into the smart glasses and playing audio from the speaker.

[0633] Step 3:

[0634] The user's voice and facial expressions are captured by the terminal's camera and microphone. This data is used as input by an emotion recognition engine, which analyzes it to identify the worker's emotional state. The output is digital data related to the current emotional state.

[0635] Specifically, the collected audio and video data is sent to an emotion recognition engine in the cloud for analysis.

[0636] Step 4:

[0637] The server receives the worker's emotional state from the emotion recognition engine and uses this information to optimize picking routes and work instructions. The input for this step is emotional state data, and the output is the adjusted work instructions.

[0638] The specific actions include route optimization using algorithms based on sentiment data and the generation of new instruction data.

[0639] Step 5:

[0640] When a user completes a task, they send their feedback to the server. This generates accumulated data that can be used to optimize the next picking route. The input is the worker's feedback, and the output is an update of the data used for future optimization.

[0641] The specific actions include displaying a short questionnaire on smart glasses after the work is completed, and sending the responses to a server for storage in a database.

[0642] 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.

[0643] 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.

[0644] 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.

[0645] [Fourth Embodiment]

[0646] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0647] 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.

[0648] 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).

[0649] 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.

[0650] 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.

[0651] 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).

[0652] 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.

[0653] 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.

[0654] 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.

[0655] 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.

[0656] 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.

[0657] 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.

[0658] 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".

[0659] In an embodiment of the present invention, a system is provided for determining the efficient route of transport vehicles in real time. The system mainly consists of a server, a terminal, and a user.

[0660] The server receives location information from GPS devices installed in the transport vehicles. It also obtains the latest traffic and weather conditions from external traffic and weather information APIs. This data is analyzed by a generated AI on the server, and the optimal route for each transport vehicle is calculated based on an optimization algorithm. The generated optimal route is then transmitted to the vehicle's terminal.

[0661] The terminal receives optimal route information transmitted from the server and presents it to the driver (user). This is done through visual map displays and voice guidance. The terminal is equipped with a chatbot function as a means of interaction, and can receive questions from the driver. For example, if the driver encounters traffic congestion or unexpected road closures, they can request alternative route suggestions through the terminal.

[0662] Technically, the server can recalculate the route as needed and send the updated route to the terminal. This two-way communication ensures that drivers always follow the optimal route for deliveries. Once a delivery is complete, the terminal sends a delivery completion notification to the server, which includes feedback on the actual arrival time and any problems encountered along the route.

[0663] As a concrete example, consider a scenario where a user has multiple delivery destinations within the Tohoku region on a given day. The server analyzes traffic conditions and weather data for each destination and generates the shortest route. If an unexpected weather change is reported along the way, the server calculates an alternative route in response to the user's request and provides new directions to the terminal. After delivery is complete, the user sends feedback to the server via the terminal, which is used to optimize future routes.

[0664] In this way, the system of the present invention enables efficient logistics operations even under time constraints imposed by legal revisions.

[0665] The following describes the processing flow.

[0666] Step 1:

[0667] The server collects real-time data, including location information, from the GPS devices of transport vehicles. It also obtains road congestion information from a traffic information API and the latest weather information from a weather information API. This data is stored in the server's database and used for later analysis.

[0668] Step 2:

[0669] The server analyzes the accumulated data using a generating AI to evaluate the current conditions of each transport vehicle. Here, the optimal transport route is calculated, taking into account the shortest route to the destination, expected traffic conditions, and weather conditions. A route optimization algorithm is used to generate and evaluate multiple route options.

[0670] Step 3:

[0671] The server selects the most efficient route and transmits this information to the vehicle's terminal. This information includes details of the route, estimated arrival time, and points requiring special attention.

[0672] Step 4:

[0673] The terminal displays the received route information to the driver. The driver can check the details of the current route through visual map displays and voice guidance.

[0674] Step 5:

[0675] If a user (driver) encounters an unexpected situation (such as traffic congestion or a road closure) while driving, they can request a new route from the server via their device. This request is made through a chatbot function.

[0676] Step 6:

[0677] The server re-evaluates the data in real time upon request from the driver and recalculates the transport route as needed. Once the new optimal route is determined, it is sent to the terminal.

[0678] Step 7:

[0679] The user (driver) receives the newly provided route guidance and resumes driving based on that information.

[0680] Step 8:

[0681] After delivery is complete, the user sends feedback to the server via their device regarding the actual arrival time and delivery route. This information is used to improve the accuracy of route optimization for future deliveries.

[0682] (Example 1)

[0683] 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".

[0684] There is a need to support drivers in executing deliveries along the optimal route while responding to real-time changes in traffic and weather conditions in transportation operations. However, conventional systems have problems such as difficulty in flexibly responding to sudden changes in traffic information and weather, and the inability to immediately recalculate based on driver input. This leads to challenges such as decreased transportation efficiency and delays.

[0685] 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.

[0686] In this invention, the server includes processing means for analyzing location data received from transport equipment and traffic and air quality information obtained from external information sources, and deriving the optimal transport route based on generative AI technology; communication means for transmitting the derived transport route information to the operator of the transport equipment; and dialogue means for receiving requests from the operator and recalculating and revising the transport route according to the situation. This enables efficient and flexible operation of transport routes that take into account environmental information that changes in real time.

[0687] "Transportation equipment" refers to vehicles and devices used for the purpose of transporting goods, and this includes trucks, buses, and the like.

[0688] "Location data" refers to the current geographical location information of a transport device, which is usually obtained via a GPS device.

[0689] "External information sources" refer to third-party services and databases that provide traffic information and air quality data, and are often accessed via APIs.

[0690] "Traffic conditions" refer to information indicating the degree of road congestion and the presence or absence of traffic jams, and are factors that affect the movement of transportation equipment.

[0691] "Atmospheric conditions" refers to information about weather and climate, including factors that affect the safety and efficiency of transportation.

[0692] "Generative AI technology" refers to technology that uses artificial intelligence to analyze large amounts of data and generate information suitable for a specific purpose.

[0693] A "transportation route" refers to the path or route used by transportation equipment to move from a specific point to its destination.

[0694] "Processing means" refers to functions or devices for analyzing received data and performing necessary calculations.

[0695] "Communication methods" refer to technologies and devices for sending and receiving information, and this includes wireless communication and network communication.

[0696] "Dialogue means" refers to devices or systems that have the function of receiving input from users and responding or performing operations based on that information.

[0697] "Information storage means" refers to technologies and devices that store collected data and retain it for later analysis and use.

[0698] This invention relates to a system that processes various types of information, including location data of transportation equipment, in real time and calculates the optimal transportation route. This system mainly consists of three elements: a server, a terminal, and a user.

[0699] The server first receives location data from GPS devices installed in the transport equipment. It also obtains the latest traffic and weather data from external information sources such as traffic APIs and weather APIs. This data is analyzed using generative AI technology to calculate the optimal transport route, taking traffic information and weather conditions into account. During this process, the server prompts the generative AI model with the message, "Based on the current location and destination, please suggest the shortest route considering traffic and weather." The optimized transport route is then transmitted to the terminal using communication technology.

[0700] The terminal receives transportation route information transmitted from the server and presents it to the user (driver) through visual map displays and voice guidance. The terminal also features a chatbot function as a means of interaction with the driver. If the driver encounters traffic congestion or road closures, the terminal can request the server to calculate alternative routes again.

[0701] The user, acting as the driver, transports the goods according to the route displayed on the terminal. After completing the transport, the driver sends arrival information and feedback about any obstacles encountered along the route to the server via the terminal. This feedback is collected by an information storage system and used to optimize future transport routes.

[0702] As a concrete example, consider a scenario where a driver makes deliveries to multiple locations within a wide area on a given day. In this case, the server analyzes traffic and weather conditions at each location and generates the optimal route. If the weather suddenly changes along the way, the driver can receive a suggestion for a new route via a terminal and continue deliveries. In this way, the system achieves efficient transportation and solves challenges in transportation operations.

[0703] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0704] Step 1:

[0705] The server receives current location data from GPS devices installed on the transport equipment. The input is raw location information obtained from the GPS device, which the server receives and processes. The server formats this location data and converts it into a standard format for use in the next step. The output is location data in a parseable format.

[0706] Step 2:

[0707] The server obtains the latest traffic and weather data via external information sources such as traffic and weather APIs. Input is the response from the APIs, typically provided in data formats such as JSON. The server receives this data in real time, extracts the necessary information, and formats it for analysis. The output is situational information combining traffic and weather data.

[0708] Step 3:

[0709] The server uses generative AI technology to analyze location data and contextual information. Standardized location data and organized contextual information are provided to the generative AI model as input. The prompt used is "Based on the current location and destination, suggest the shortest route considering traffic and weather." The output is the optimal transport route calculated by the optimization algorithm.

[0710] Step 4:

[0711] The server transmits the calculated optimal transportation route information to the terminal. This communication utilizes a secure protocol to maintain the consistency and security of the transmitted data. The input is the calculated route information, and the output is the optimal route information received by the terminal.

[0712] Step 5:

[0713] The terminal displays received transportation route information to the user. Input is route information received from the server, and output is a visual map display and voice guidance. The terminal utilizes chatbot functionality to accept additional requests and questions from the user. This allows the terminal to provide users with efficient and safe transportation guidance.

[0714] Step 6:

[0715] If a user encounters a new situation during transport, such as traffic congestion or a sudden change in weather, they can use their terminal to request the server to calculate an alternative route. The input is the user's request information, and the output is the newly calculated transport route information. The server then uses the regenerated AI model to quickly calculate a route suitable for the situation and sends it to the terminal.

[0716] Step 7:

[0717] When a user completes a transportation task, they send a completion notification and feedback from their terminal to the server. The input consists of arrival information upon completion of the task and feedback on any problems encountered. The server receives this information and uses it to improve future route calculations. The output is the improvement information stored in the database.

[0718] (Application Example 1)

[0719] 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".

[0720] In the food delivery industry, delays due to traffic conditions and weather changes are a major challenge, leading to decreased customer satisfaction. Furthermore, selecting efficient delivery routes is difficult, placing a heavy burden on delivery personnel. A system is needed to address these challenges and achieve fast and efficient delivery.

[0721] 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.

[0722] In this invention, the server includes a computing means that acquires location data, traffic data, and weather data of a means of transport in real time, analyzes this data to generate an efficient transport route, an information transmission means that provides the generated transport route information to a person operating the means of transport, and an interaction means that receives requests from the operator and recalculates and updates the transport route as necessary. This makes it possible to deliver via the optimal route at all times.

[0723] "Real-time" refers to a timeframe in which information is processed and presented instantaneously.

[0724] "Transportation means" refers to any device or system used to move goods or people from one point to another.

[0725] "Location data" refers to information that indicates the current geographical coordinates of a specific object or person.

[0726] "Traffic data" refers to information about the flow of vehicles on roads, including conditions such as congestion and accidents.

[0727] "Weather data" refers to information about weather conditions, including temperature, precipitation, and wind speed.

[0728] "Analysis" is a detailed investigation that breaks down data and helps understand its patterns and trends.

[0729] An "efficient transport route" is a route designed to reach the destination safely and quickly in the shortest possible time.

[0730] "Computational means" refers to a device or program that has the ability to process data for a specific purpose.

[0731] "Information transmission means" refers to a process or system for effectively transmitting data or information to a person or device.

[0732] "Means of interaction" refer to the mechanisms or programs that enable humans and systems to communicate.

[0733] "Optimization" is the process of using resources in the most efficient way to achieve a goal.

[0734] The system for implementing the present invention has the ability to acquire location data, traffic data, and weather data of a means of transport in real time, analyze them, and generate the most efficient transport route. The server acquires location data from GPS devices installed on the means of transport and collects traffic and weather data using external data services. Services such as the Google Maps API and OpenWeather API are used for this purpose. This data is analyzed and optimized using a custom algorithm written in Python. The generated transport route information is transmitted to the user's terminal operating the means of transport via Wi-Fi or a cellular network.

[0735] The terminal provides this information to the user through visual map displays and voice guidance. Users can also ask questions and make requests in real time using the terminal's built-in chatbot function. This interaction allows users to quickly respond to problems or changes that occur during transport. After delivery is complete, the terminal sends feedback from the user and recipient to a server, and this information is used to optimize future transport routes.

[0736] As a concrete example, in food delivery, there is a need for the automatic generation of efficient routes during peak hours. The server reflects traffic congestion and weather changes in real time, helping delivery personnel deliver goods to multiple destinations within a set time. In this case, the food delivery application presents the user with the latest route and notifies them immediately if there are any changes.

[0737] As an example of a specific prompt for the generating AI model, the following is used: "For the concentrated orders in the Shinjuku area, calculate an efficient delivery route based on current traffic conditions and weather data. Provide the optimal route for the next 30 minutes and notify the delivery driver by voice."

[0738] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0739] Step 1:

[0740] The server receives real-time location data from GPS devices installed on the means of transport. The input is geographic coordinates (latitude and longitude) provided by the GPS device. The server extracts this data and stores it temporarily. The output is the current, accurate location data, which is then recognized by the server.

[0741] Step 2:

[0742] The server uses external API services (e.g., Google Maps API, OpenWeather API) to retrieve current and predicted traffic and weather data. The input consists of queries to the APIs, including location data as a parameter. The server analyzes the data retrieved from these APIs to identify traffic congestion and weather conditions. The output provides traffic and weather conditions for a specific area.

[0743] Step 3:

[0744] The server uses a generative AI model to calculate the optimal transport route based on acquired location data, traffic data, and weather data. The input is the dataset obtained in the previous steps. The generative AI model processes this data and calculates the route that maximizes efficiency. The output is the optimized transport route.

[0745] Step 4:

[0746] The server sends the generated optimal transport route to the user's terminal operating the transport. As input, route information is formatted and transmitted via communication. The terminal receives this information and prepares to provide visual and audio guidance to the user. As output, real-time route guidance data is displayed on the terminal.

[0747] Step 5:

[0748] The user performs transportation based on information provided via the terminal. They may also use the chatbot function to request a route recalculation if necessary. Input includes user feedback and additional instructions. The terminal processes these requests and sends new requests to the server. The output is the updated transportation route, if necessary, displayed again on the terminal.

[0749] Step 6:

[0750] Once delivery is complete, the user enters a delivery completion report into the terminal. This input includes information such as confirmation of successful delivery and any problems encountered during transit. The terminal collects this feedback and sends it back to the server. As output, the server accumulates feedback information that will be used for future optimizations.

[0751] 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.

[0752] In embodiments of the present invention, a system is provided that not only determines the efficient route of transport vehicles in real time, but also analyzes the user's emotions and adapts the system's operation accordingly. The system mainly consists of a server, terminals, users, and an emotion engine.

[0753] The server is responsible for collecting location, traffic, and weather information related to transport vehicles. This data is analyzed by a generative AI and forms the basis for calculating the optimal transport route. The calculated optimal route information is then transmitted to the terminals of each transport vehicle for use.

[0754] The device has a built-in chatbot function for interacting with the driver (user). Using this function, users can request new routes as needed while driving. Furthermore, the device incorporates an emotion engine that can understand the user's emotional state in real time through speech recognition and text analysis.

[0755] The emotion engine recognizes the user's emotions, which are then sent to the server and used to adjust the optimal route and select the appropriate communication method as needed. For example, if the system determines that the user is feeling stressed or anxious, it can select and provide a calmer voice guidance.

[0756] For example, if a user reports fatigue due to long hours of driving, the system detects this emotion through its emotion engine. The server then prioritizes guiding the user to an immediately available rest area and provides this information to the terminal. If the user is relaxed, the system continues with normal guidance.

[0757] After delivery is complete, feedback, including user emotional data, is stored on the server. This information is used to improve the accuracy of future optimization algorithms, enabling more effective support for operations. As a result, the system of this invention achieves efficient logistics while reducing the psychological stress on drivers.

[0758] The following describes the processing flow.

[0759] Step 1:

[0760] The server collects location information from GPS devices installed in transport vehicles. It also obtains the latest road and weather information via traffic and weather APIs. This data is integrated on the server and used for real-time route optimization.

[0761] Step 2:

[0762] The server analyzes the collected data using generating AI to evaluate the operating conditions of each transport vehicle. This includes a process of selecting the optimal transport route, taking into account the shortest route to the destination, congestion levels, and weather conditions.

[0763] Step 3:

[0764] The server transmits the generated optimal route information to the terminal of each transport vehicle. This information includes specific route details, estimated travel time, and important notes.

[0765] Step 4:

[0766] The terminal receives route information from the server and presents it to the user through a visual map display and voice guidance. The terminal also incorporates an emotion engine that analyzes the user's emotional state while driving based on their input.

[0767] Step 5:

[0768] If a user reports stress or fatigue while driving, the device uses an emotion engine to detect that emotion and sends feedback to the server.

[0769] Step 6:

[0770] The server analyzes the user's emotional state received from the emotion engine and adjusts the route and guidance messages as needed. For example, it might guide the user to a relaxing resting place or switch to a calmer voice guidance.

[0771] Step 7:

[0772] When a user requests a new route, the device sends the request to the server. The server immediately recalculates the route and provides the device with the new directions.

[0773] Step 8:

[0774] After delivery is complete, the user sends arrival time, delivery route, and emotional feedback to the server via their device. This information is stored in the server's database and used to optimize future delivery plans.

[0775] (Example 2)

[0776] 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".

[0777] In modern times, while there is a demand for increased efficiency in transport vehicles, there is a challenge in that operational support that takes into account the psychological state of drivers is not adequately provided. Therefore, there is a need to develop systems that are efficient while reducing driver stress.

[0778] 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.

[0779] This invention includes a server that includes a processing means for acquiring location information, traffic information, and weather information related to transport vehicles in real time and analyzing these multiple pieces of information to generate an optimal transport route; an adaptive means for analyzing the driver's emotions and adaptively changing voice guidance and route guidance based on that; and a data storage means for collecting feedback, including the driver's emotional data, after delivery is completed and utilizing it for optimizing the next transport route. This makes efficient transport possible while reducing the psychological burden on the driver.

[0780] "Real-time" refers to the instantaneous acquisition or processing of information and data without delay.

[0781] A "transport vehicle" is a machine or device used to transport goods or passengers, and generally includes those that primarily travel on roads or railways.

[0782] "Location information" refers to data that indicates the geographical location of a specific object or person, and is usually represented by latitude and longitude.

[0783] "Traffic information" refers to data on vehicle flow, traffic congestion, and road conditions.

[0784] "Weather information" refers to data related to meteorological conditions, including information such as temperature, precipitation, and wind speed.

[0785] "Processing means" refers to a method or apparatus for receiving, processing, analyzing, and interpreting data.

[0786] A "generative AI model" refers to a framework of artificial intelligence that performs pattern recognition and information generation based on large amounts of data.

[0787] A "prompt message" refers to an input sentence used to give instructions or questions to an AI model.

[0788] "Communication means" refers to methods and devices for sending and receiving information.

[0789] "Dialogue means" refers to methods and devices for users and systems to exchange information and instructions.

[0790] "Emotional analysis" refers to estimating a person's psychological state based on information such as audio and text.

[0791] "Adaptive measures" refer to methods or devices for changing or adapting systems or processes according to the situation.

[0792] "Data storage means" refers to methods and devices for storing collected information and data for later use.

[0793] This invention is a system in which a server, terminal, and user interact with each other to improve the efficiency of transport vehicle operations in real time, and to provide support tailored to the driver's psychological state.

[0794] The server first collects necessary data using the vehicle's location information, traffic information, and weather information. GPS devices and various APIs are used for this data collection. For example, traffic information is obtained using the Google Maps API, and weather data is acquired using weather information APIs. Next, the server analyzes this data using a generative AI model to generate the optimal transport route. The generated route information is then transmitted to the driver's terminal via communication means.

[0795] The terminal is a central element supporting interaction with the driver. Chatbot functionality allows drivers to interact with the terminal via voice or text, obtaining information during their journey and issuing instructions for route changes. Furthermore, a built-in emotion analysis engine analyzes the driver's voice and text to estimate their psychological state. For example, it uses a speech recognition API to analyze the driver's tone and detect signs of stress or fatigue.

[0796] If a user reports feeling fatigued due to prolonged driving, the server uses emotional information and a generative AI model to generate a prompt such as, "Guide me to the best route from my current location to the nearest rest stop." Based on this prompt, the driver is provided with appropriate route guidance. If the user is relaxed, the system continues with normal guidance.

[0797] As a result, this system can reduce the psychological burden on drivers and enable efficient transportation.

[0798] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0799] Step 1:

[0800] The server collects location information of transport vehicles obtained from GPS devices, traffic information from traffic APIs, and weather information from weather APIs. This information is input to the server as raw data and stored in a database. This stored data forms the basis for subsequent analysis and route calculations.

[0801] Step 2:

[0802] The server inputs the collected data into a generating AI model to calculate the optimal transport route. Specifically, it determines the main route from the current vehicle's location to its destination based on location and traffic information, and then adapts that route considering weather conditions. The outputted optimal route information becomes the data that will be transmitted to the driver in the next step.

[0803] Step 3:

[0804] The server sends the generated optimal route information to the driver's terminal. This uses a mechanism that updates the data in real time using a communication protocol. The route information received by the terminal is displayed for the user's reference.

[0805] Step 4:

[0806] The terminal initiates a conversation with the driver, using a chatbot function to accept voice commands and text input from the driver. If the driver requests a new route, the terminal resends the request to the server to prompt a route recalculation. The input at this time is a new route request, and the output is the new route information.

[0807] Step 5:

[0808] The device activates an emotion engine and analyzes voice and text from the driver. This analysis is performed to determine the driver's stress and fatigue levels, and emotional data is output. This helps to understand the driver's psychological state and assists in taking the next steps.

[0809] Step 6:

[0810] The server receives emotion data sent from the terminal and communicates it to the AI ​​model that generates new prompts, thereby formulating appropriate countermeasures. For example, a prompt such as "Provide calm navigation" can be output by the system, enabling it to provide guidance tailored to the driver's psychological state.

[0811] Step 7:

[0812] After delivery is complete, user feedback is sent from the terminal to the server. This feedback includes driver sentiment data and is used as a data accumulation tool. The accumulated data is used to optimize future routes and improve the system.

[0813] (Application Example 2)

[0814] 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".

[0815] Optimizing both operational efficiency and worker stress simultaneously in a logistics center is not easy. In particular, proceeding with work while ignoring the emotional state of individual workers can lead to decreased efficiency and increased errors. Therefore, it is necessary to understand workers' emotional states in real time and provide work instructions accordingly.

[0816] 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.

[0817] In this invention, the server includes information processing means that acquire location information and traffic information of transport devices in real time and analyze this data to generate an optimal transport route; emotion recognition means that identify the emotional state based on the user's voice and facial expression data and adjust the guidance method accordingly; and communication means that provide the generated transport route information to the user. This makes it possible to provide optimal work instructions in a logistics center according to the emotional state of the workers.

[0818] "Real-time" is a concept that refers to information and actions being processed immediately and used without delay when needed.

[0819] "Transportation equipment" refers to a machine or system used to transport goods or people to a designated location.

[0820] "Location information" refers to data that indicates the geographical coordinates or location of a specific object.

[0821] "Traffic information" refers to data on road and public transportation congestion and operating conditions.

[0822] "Information processing means" refers to devices and programs used to analyze data and extract and process necessary information.

[0823] "Voice and facial expression data" refers to digital information about the characteristics of the user's voice and facial expressions.

[0824] "Emotional state" refers to the psychological or emotional state that an individual is currently experiencing.

[0825] "Emotion recognition means" refers to technologies and devices that analyze and identify an individual's emotional state based on data such as voice and facial expressions.

[0826] "Communication means" refers to the technology or equipment used to send and receive information.

[0827] "Information storage means" refers to a device or system for securely storing data and information for later use.

[0828] An example of this invention's application is a system designed to improve work efficiency and reduce worker stress in a logistics center. This system aims to provide workers with real-time information using smart glasses.

[0829] The server collects inventory data, shelf location information, and worker location information within the logistics center. This data is analyzed by a generative AI model to generate the optimal picking route. The server also combines this data with environmental data to determine the most efficient work procedure. The specific software used includes OpenAI as the generative AI model and Amazon Rekognition as the emotion recognition engine.

[0830] The device refers to smart glasses that visualize the generated picking route for the worker and provide voice guidance. The glasses' built-in camera and microphone collect the worker's voice and facial expression data, and analyze their emotional state in real time. Based on their emotions, the system adjusts the work route and suggests breaks to reduce workload.

[0831] For example, when a staff member at a logistics center is picking complex items, if the system detects that the worker is fatigued, the server will immediately adjust the work route and suggest a break. An example of a prompt message would be, "Based on staff emotional data, please suggest the optimal method for adjusting the workload."

[0832] This system makes it possible for workers within the logistics center to perform their tasks efficiently and effectively.

[0833] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0834] Step 1:

[0835] The server collects inventory, shelf locations, and worker location information within the logistics center. Using this data as input, a generative AI model calculates the optimal picking route. As output, customized route information is generated for each worker.

[0836] In terms of specific operations, it executes queries to the database to retrieve the necessary data, and then passes that data to the generative AI model for analysis.

[0837] Step 2:

[0838] The smart glasses, acting as the terminal, display the picking route received from the server in the worker's field of vision and also provide voice guidance. The input for this process is route information from the server, and the output is the presentation of information to the worker.

[0839] The specific actions include displaying graphics on a display built into the smart glasses and playing audio from the speaker.

[0840] Step 3:

[0841] The user's voice and facial expressions are captured by the terminal's camera and microphone. This data is used as input by an emotion recognition engine, which analyzes it to identify the worker's emotional state. The output is digital data related to the current emotional state.

[0842] Specifically, the collected audio and video data is sent to an emotion recognition engine in the cloud for analysis.

[0843] Step 4:

[0844] The server receives the worker's emotional state from the emotion recognition engine and uses this information to optimize picking routes and work instructions. The input for this step is emotional state data, and the output is the adjusted work instructions.

[0845] The specific actions include route optimization using algorithms based on sentiment data and the generation of new instruction data.

[0846] Step 5:

[0847] When a user completes a task, they send their feedback to the server. This generates accumulated data that can be used to optimize the next picking route. The input is the worker's feedback, and the output is an update of the data used for future optimization.

[0848] The specific actions include displaying a short questionnaire on smart glasses after the work is completed, and sending the responses to a server for storage in a database.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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."

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] The following is further disclosed regarding the embodiments described above.

[0871] (Claim 1)

[0872] A processing means that acquires real-time location information and traffic information of transport vehicles, and executes a program that analyzes this data to generate the optimal transport route,

[0873] A communication means for providing generated transport route information to the driver of the transport vehicle,

[0874] A dialogue means that accepts input from the driver and recalculates and updates the transport route as needed,

[0875] A data storage system that collects feedback from drivers and recipients after delivery is completed and uses it to optimize the next transportation route,

[0876] A system that includes this.

[0877] (Claim 2)

[0878] The system according to claim 1, which provides information to the driver's terminal in a chat format and responds to the driver's questions.

[0879] (Claim 3)

[0880] The system according to claim 1, which further optimizes the route by taking into account weather information and information on cargo in transit.

[0881] "Example 1"

[0882] (Claim 1)

[0883] A processing means that analyzes location data received from transportation equipment and traffic and air quality information obtained from external sources, and derives the optimal transportation route based on generative AI technology,

[0884] A communication means for transmitting the derived transportation route information to the operator of the transportation equipment,

[0885] A dialogue mechanism that receives requests from operators and recalculates and revises the transport route according to the situation,

[0886] An information storage means that collects information from the operator and recipient after the completion of transportation operations to support the optimization of the next transportation route,

[0887] A system that includes this.

[0888] (Claim 2)

[0889] The system according to claim 1, which provides information in an interactive format on the operator's terminal and responds to the operator's inquiries.

[0890] (Claim 3)

[0891] The system according to claim 1, which performs further route optimization taking into account atmospheric conditions and the attributes of the goods being transported.

[0892] "Application Example 1"

[0893] (Claim 1)

[0894] A computing means that acquires real-time location data, traffic data, and weather data of transportation means, analyzes this data to generate efficient transportation routes,

[0895] An information transmission means that provides generated transportation route information to a person operating the means of transport,

[0896] Interaction means that receive requests from the operator and recalculate and update the transport route as needed,

[0897] A means of storing information that collects feedback from the person operating the system and the recipient after the completion of transportation, and that contributes to optimizing the next transportation route.

[0898] A system that includes this.

[0899] (Claim 2)

[0900] The system according to claim 1, which provides information in an interactive format to the terminal of the person performing the operation and responds to questions from the person performing the operation.

[0901] (Claim 3)

[0902] The system according to claim 1, which further optimizes the route by taking into account weather data and information on goods in transit.

[0903] "Example 2 of combining an emotion engine"

[0904] (Claim 1)

[0905] A processing means that acquires location information, traffic information, and weather information related to transport vehicles in real time, analyzes these multiple pieces of information to generate the optimal transport route,

[0906] A communication means for providing generated transport route information to the driver of the transport equipment,

[0907] A dialogue system that receives instructions from the driver and recalculates and updates the transport route as needed,

[0908] Adaptive means that analyze the driver's emotions and adaptively change voice guidance and route guidance based on that analysis,

[0909] A data storage method that collects feedback, including driver emotional data, after delivery is completed, and uses this data to optimize the next transport route.

[0910] A system that includes this.

[0911] (Claim 2)

[0912] The system according to claim 1, which provides information to the driver's information processing device in an interactive manner, responds to the driver's inquiries, analyzes the driver's emotional state, and modifies its response accordingly.

[0913] (Claim 3)

[0914] The system according to claim 1, which takes weather information and cargo information in transit into consideration, and further generates prompt messages using a generative AI model to perform route optimization.

[0915] "Application example 2 of combining emotional engines"

[0916] (Claim 1)

[0917] An information processing means that acquires location information and traffic information of transportation devices in real time, analyzes this data to generate the optimal transportation route,

[0918] An emotion recognition means that identifies the user's emotional state based on their voice and facial expression data, and adjusts the guidance method accordingly,

[0919] A communication means for providing the generated transportation route information to the user,

[0920] A dialogue mechanism that accepts input from the user and recalculates and updates the transport route as needed,

[0921] A means of accumulating information to collect feedback from users and others after the work is completed and to use it to optimize the next transportation route,

[0922] A system that includes this.

[0923] (Claim 2)

[0924] The system according to claim 1, which provides information to the user's visual device in an interactive manner and responds to the user's requests.

[0925] (Claim 3)

[0926] The system according to claim 1, which further optimizes the route by taking into account environmental information and information on items being transported. [Explanation of Symbols]

[0927] 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 processing means that acquires real-time location information and traffic information of transport vehicles, and executes a program that analyzes this data to generate the optimal transport route, A communication means for providing generated transport route information to the driver of the transport vehicle, A dialogue means that accepts input from the driver and recalculates and updates the transport route as needed, A data storage system that collects feedback from drivers and recipients after delivery is completed and uses it to optimize the next transportation route, A system that includes this.

2. The system according to claim 1, which provides information to the driver's terminal in a chat format and responds to the driver's questions.

3. The system according to claim 1, which further optimizes the route by taking into account weather information and information on cargo in transit.

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