system

Abstract

We provide the system. [Solution] A means of integrating traffic information, weather data, and material inventory information into a database, A means of analyzing data acquired in real time to determine delivery priorities, A means of generating the optimal material delivery route using a generative artificial intelligence model, Means for providing the generated delivery route to a display device, 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 and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The prompt delivery of materials during disasters is often hindered by many traffic obstacles and sudden weather changes. Also, it is a difficult task to appropriately judge high-priority materials and accurately deliver them to the required locations. In such a situation, the conventional manual method may take too much time, and a prompt response is required. Therefore, there is a need for a technology that can utilize real-time data to efficiently and flexibly formulate and execute a delivery plan for materials.

Means for Solving the Problems

[0005] This invention provides a system that centrally integrates traffic information, weather data, and material inventory information, and analyzes this data in real time. This system determines optimal delivery priorities based on traffic and weather conditions and automatically generates optimal material delivery routes using a generative artificial intelligence model. Furthermore, by providing these generated delivery routes to a display device, it enables rapid and effective material delivery. The system also includes means for automatically adjusting high-priority material delivery plans and means for dynamically readjusting delivery routes based on user feedback, thereby enhancing the flexibility and execution of the plan.

[0006] "Traffic information" refers to data that includes information such as current road conditions, traffic volume, and road closures due to accidents or construction.

[0007] "Weather data" refers to data that shows various weather-related information, such as current weather conditions, temperature, precipitation, wind speed, and wind direction.

[0008] "Inventory information for goods" refers to information regarding the type, quantity, and storage conditions of goods in a specific warehouse or facility.

[0009] "Methods for integrating into a database" refers to the function of centrally managing and organizing information obtained from different data sources, and maintaining consistency in that data.

[0010] "A means of analyzing data acquired in real time to determine delivery priorities" refers to a function that instantly analyzes the latest data to determine which goods should be delivered with priority.

[0011] "Generative means" refers to a function that uses algorithms or programs to produce results or outputs that meet a specific purpose.

[0012] "Methods for generating optimal material delivery routes using generative artificial intelligence models" refers to a function that uses AI technology to calculate the optimal route for material delivery while considering various constraints.

[0013] "Means of providing information to a display device" refers to a function that transmits and displays calculation results and data on a display or screen for visually presenting information to the user.

[0014] "Means for automatically adjusting high-priority material delivery plans" refers to a function that autonomously updates and modifies existing delivery plans based on unforeseen circumstances or new information.

[0015] "Means of receiving user feedback and dynamically readjusting delivery routes" refers to flexible functionality that allows existing routes to be modified based on user input and opinions. [Brief explanation of the drawing]

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

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

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

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

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

[0021] In the following embodiments, the 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.

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

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

[0024] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0037] This invention is a system that collects and integrates traffic information, weather data, and material inventory information in real time to enable effective material distribution. This system has a three-tiered structure consisting of a server, terminals, and users, each playing a different role.

[0038] The server forms the core of this system, collecting and analyzing data. It acquires real-time data from traffic information services, weather forecasting services, and warehouse management systems, and integrates it. Using this integrated data, the system can quickly set delivery priorities for goods. Furthermore, the server utilizes a generative AI model to automatically generate optimal delivery routes that take into account traffic congestion and weather conditions.

[0039] The terminal functions as a user interface, providing a means for the user to input necessary information. Through the terminal, the user can input the type, quantity, and priority of necessary supplies. The terminal transmits the input information to the server and visually displays the generated delivery plan.

[0040] Users can view the delivery plan provided by the server through their terminal and provide feedback as needed. The server uses this feedback to dynamically adjust delivery routes and priorities, increasing the flexibility of the plan.

[0041] As a concrete example, consider a scenario involving a large-scale natural disaster. The server acquires traffic and weather data from the affected area and generates the optimal delivery route based on the priority of supplies. In doing so, it takes into account road closures and severe weather conditions, while ensuring priority delivery to evacuation centers that are short on supplies. The terminal presents this delivery plan to the user, and the server flexibly modifies the plan based on additional requests and modification instructions from the user.

[0042] This system is expected to enable the rapid and effective delivery of supplies during disasters.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The server retrieves traffic information, weather data, and material inventory information in real time from multiple external data sources. This includes obtaining information from traffic APIs and weather APIs.

[0046] Step 2:

[0047] The server centrally integrates the acquired data, formats it, and organizes it into a consistent database. This eliminates duplicate data and standardizes the format.

[0048] Step 3:

[0049] Users input the type, quantity, and priority of necessary supplies via a terminal. This information is sent to the server as basic data for the supply delivery plan.

[0050] Step 4:

[0051] The server analyzes real-time traffic and weather data to determine delivery priorities. This includes analysis that takes into account congestion and weather conditions.

[0052] Step 5:

[0053] The server uses a generative artificial intelligence model to generate the optimal delivery route. This AI model calculates the route that maximizes delivery efficiency based on the acquired data.

[0054] Step 6:

[0055] The terminal receives the delivery route generated from the server and displays it in the user interface. Users can view visualized maps and detailed lists.

[0056] Step 7:

[0057] Users can check delivery routes via their devices and send feedback or change requests to the server as needed. This feedback can include specific delivery needs and changes.

[0058] Step 8:

[0059] The server receives user feedback, re-evaluates delivery routes, and dynamically adjusts them as needed. This enables flexible and adaptive delivery planning.

[0060] Step 9:

[0061] The terminal then provides the user with the updated delivery plan again and supports final confirmation and approval. This ensures reliable delivery of goods.

[0062] (Example 1)

[0063] 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."

[0064] There is a need to respond quickly to changes in traffic conditions, weather conditions, and fluctuations in material inventory, and to deliver goods efficiently. However, conventional systems have difficulty integrating this diverse information in real time, making it difficult to flexibly generate and adjust optimal delivery routes. Furthermore, there is a lack of mechanisms to appropriately revise plans based on dynamic feedback from users, resulting in problems with effective material delivery.

[0065] 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.

[0066] In this invention, the server includes means for integrating traffic information, weather data, and inventory data into a data set; means for analyzing information acquired in real time and determining delivery priorities; and means for generating an optimal transportation route based on prompt messages using a generative artificial intelligence model. This makes it possible to formulate an optimal delivery plan based on information that changes in real time and to flexibly adjust the plan by utilizing user feedback.

[0067] "Traffic information" refers to information that shows the flow of traffic, congestion levels, and passable routes.

[0068] "Weather data" refers to information related to weather, such as weather conditions, forecasts, temperature, and precipitation.

[0069] "Inventory data" refers to management information such as the quantity and location of goods.

[0070] A "data set" is a collection of information that integrates multiple data points obtained from different sources.

[0071] "Real-time" refers to a state where the time between information acquisition and processing is instantaneous.

[0072] A "generative artificial intelligence model" is a computer program that implements algorithms for making predictions and optimizations based on input data.

[0073] A "prompt" is a text that describes instructions or questions given to a generative artificial intelligence model in order to produce an appropriate output.

[0074] A "transportation route" refers to a specific path or route planned for the efficient movement of goods.

[0075] This invention is a system that integrates traffic information, weather data, and inventory data in real time to achieve optimal material delivery. This system mainly consists of three elements: a server, terminals, and users.

[0076] The server functions as the central hub of this system, collecting information from diverse data sources. Specifically, it utilizes traffic information services to understand traffic conditions, weather forecasting services to obtain weather predictions, and a warehouse management system to manage inventory levels. This data is integrated into the server and analyzed immediately. A generative AI model is used for analysis, and appropriate transportation routes are calculated based on the input of prompts. An example of a prompt is, "Consider the traffic and weather data for the specified area, and create the optimal supply delivery route to facilities experiencing shortages."

[0077] The terminal serves as the user interface. Through the terminal, users can input necessary material information and verify the generated transportation route. Furthermore, the terminal also acts as a medium for sending user feedback to the server.

[0078] Users can participate in the system in real time by using these terminals to efficiently manage supplies, check delivery plans, and provide feedback as needed. This participation enables the system to operate quickly and flexibly.

[0079] A concrete example is the emergency delivery of supplies during a disaster. Users input information about the supplies they need immediately into their terminals, and then review and adjust the delivery routes generated based on the latest traffic and weather data provided by the server. This enables the most effective delivery plan and allows for the immediate provision of necessary assistance.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The server retrieves data from traffic information services, weather forecasting services, and warehouse management systems. The input is raw data obtained through API requests from these services. This allows for the collection of real-time data on traffic conditions, weather conditions, and inventory levels. This data is then integrated within a database.

[0083] Step 2:

[0084] The server analyzes the acquired data to determine the priority of supply delivery. The input is the data integrated in step 1. This data is analyzed, and priorities are assigned based on specific conditions (e.g., insufficient stock, urgency). The output is a dataset with the assigned priorities.

[0085] Step 3:

[0086] The server inputs a prompt message into the generating AI model to generate the optimal transportation route. The input consists of the prioritized data obtained in step 2 and the prompt message: "Consider the traffic and weather data for the specified area and create the optimal supply delivery route to the facility experiencing shortages." Using the generating AI model, the optimal route that takes traffic congestion and weather into account is output.

[0087] Step 4:

[0088] The terminal visually displays the generated transport route to the user. The input is the transport route generated by the server in step 3. Visualizing this on a map makes it easy for the user to understand the transport plan. The output is the detailed transport route presented to the user.

[0089] Step 5:

[0090] Users provide feedback through their devices, and the delivery route is adjusted as needed. The input consists of the user's opinions and requests regarding the delivery plan presented in step 4. The output is sent to the server as a revised request and reflected in the next plan. This improves the flexibility of the delivery plan.

[0091] (Application Example 1)

[0092] 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."

[0093] To achieve rapid and efficient delivery of goods at logistics facilities, it is necessary to quickly generate and dynamically adjust optimal delivery routes that take into account traffic information and weather conditions. However, current technology does not adequately modify or optimize delivery plans in real time, leading to problems such as reduced delivery efficiency and delays. Furthermore, it is difficult to immediately incorporate feedback from delivery personnel, and the inability to flexibly adjust routes is a challenge.

[0094] 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.

[0095] In this invention, the server includes means for integrating traffic information, weather data, and inventory information of goods into an information recording device; means for analyzing the information acquired in real time and determining the delivery order; and means for generating the optimal material delivery route using a generative artificial intelligence model. This enables logistics facility managers and transportation personnel to obtain the optimal delivery route in real time and improve delivery efficiency. Furthermore, a feedback function using a communication terminal allows for dynamic modification of the delivery plan and maintenance of the optimal route.

[0096] "Traffic information" refers to real-time data on transportation conditions, such as road congestion and traffic restrictions.

[0097] "Weather data" refers to real-time numerical information about weather conditions, such as weather, temperature, precipitation, and wind speed.

[0098] "Goods inventory information" refers to data regarding the quantity and condition of goods in warehouses and logistics facilities.

[0099] An "information recording device" is a computer system or database used to integrate and store various types of data.

[0100] "Analysis" is the process of processing acquired information to derive meaningful results from data.

[0101] "Delivery order" refers to the criteria used to determine the priority and order in which goods are delivered.

[0102] A "generative artificial intelligence model" is an algorithm or program that uses machine learning to generate the optimal delivery route.

[0103] A "delivery route" is a plan that indicates the optimal route for delivering goods.

[0104] A "display device" is a device, such as a computer or smartphone, that visually displays information.

[0105] A "communication terminal" is a portable information processing device capable of data communication, such as a smartphone or tablet.

[0106] "Real-time feedback" is a communication function that instantly receives and reflects information from the sender.

[0107] "Dynamic reconstruction" refers to the process of updating and modifying existing plans and structures in real time based on new information from external sources.

[0108] The system that realizes this invention mainly consists of three components: a server, a terminal, and a user.

[0109] The server aggregates traffic information, weather data, and inventory information in real time and integrates it into an information recording device. In this process, external services such as Google Maps API and weather data APIs are used to collect information. The collected data is analyzed using a Python script, and a generative artificial intelligence model generates the optimal delivery route. This generation process includes optimization calculations that take traffic and weather conditions into account, and the generated delivery route is provided to the terminal in real time.

[0110] The terminals are smartphones and tablets used by logistics facility managers and transportation personnel. The generated delivery plans are displayed graphically on the terminals, allowing each user to plan their actions based on them. Furthermore, they feature real-time feedback, enabling dynamic modifications to the delivery plan by providing delivery status and new information to the server.

[0111] Users can check delivery routes and priorities through their devices and provide feedback as needed. This feedback is sent to the server, and the delivery plan is automatically readjusted. This feature allows for flexible and immediate responses, even in cases such as sudden changes in weather or unexpected traffic congestion.

[0112] As a concrete example, consider a logistics facility efficiently handling the peak delivery period during the Christmas season. This system makes it possible to optimize routes while minimizing delays, even while taking into account worsening weather and traffic conditions.

[0113] An example of a prompt to the generating AI model would be: "Based on current traffic data and weather conditions, generate the optimal route to the following delivery destinations. Please pay particular attention to reducing estimated arrival times in bad weather." In response to this prompt, the AI ​​will propose the optimal route.

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

[0115] Step 1:

[0116] The server collects traffic information, weather data, and inventory information in real time using the Google Maps API and weather data API. This information is integrated into an information recording device. As input, data from each API is retrieved and stored in a database. As output, an integrated dataset is prepared.

[0117] Step 2:

[0118] The server analyzes the integrated dataset using a Python script to determine the delivery order. The input is the integrated dataset from step 1, and the priority and necessity of each data point are evaluated. The output is a list of prioritized delivery tasks.

[0119] Step 3:

[0120] The server uses a generated artificial intelligence model to create the optimal delivery route using prompts. The inputs here are the prompts and the list of delivery tasks obtained in step 2. The generated AI model calculates the optimal route, and the output is a specific delivery route.

[0121] Step 4:

[0122] The server sends the generated delivery route to the terminal, which then displays it visually on the display device. The input is the delivery route generated in step 3, and the output is the plan displayed on the screen of a terminal used by logistics facility managers or transportation personnel.

[0123] Step 5:

[0124] Users can view delivery plans via their devices and provide real-time feedback as needed. Input is the displayed delivery route, and users provide feedback. Output is the user's feedback information sent to the server.

[0125] Step 6:

[0126] The server readjusts the delivery plan based on user feedback. The input here is the feedback information obtained in step 5. Through data processing, the AI ​​recalculates, and the output is an updated delivery route. This updated information is sent back to the terminal, ensuring that the optimal route is always maintained.

[0127] 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.

[0128] This invention combines a system that utilizes traffic information, weather data, and inventory information to achieve optimal material delivery with an emotion engine. The emotion engine recognizes the user's emotional state and uses this to adjust the delivery plan.

[0129] The server collects and integrates traffic information, weather data, and material inventory information as before. It performs real-time analysis of this data to determine delivery priorities. Furthermore, it can use generative artificial intelligence models to generate optimal material delivery routes based on the existing information.

[0130] The terminal is responsible for the user interface and is equipped with an emotion engine. This emotion engine evaluates the user's emotions through speech recognition, facial expression analysis, and contextual analysis. This makes it possible to quantify how the user is reacting to the delivery plan and the degree of dissatisfaction or satisfaction.

[0131] The user enters delivery plan details using a terminal and confirms the visually displayed delivery route. Simultaneously, an emotion engine analyzes the user's tone of voice and facial expressions and sends this emotion data to the server. Based on this emotion data, the server readjusts the delivery route as needed. This enables flexible delivery planning that considers not only efficiency but also the user's emotional satisfaction.

[0132] As a concrete example, suppose a user receives a delivery route that requires a detour due to heavy rain. The terminal detects the user's dissatisfaction with this using an emotion engine and transmits it to the server. The server considers this emotion data, reconsiders the route, changes priorities, and provides a plan that satisfies the user. In this way, this system combines technical optimization with improved user experience.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] The server acquires and integrates real-time traffic information, weather data, and inventory information from external sources. This creates a database based on the latest road and weather conditions.

[0136] Step 2:

[0137] The server analyzes the integrated data to determine priorities for efficient supply delivery. This analysis includes decisions that take into account weather conditions and the presence or absence of traffic disruptions.

[0138] Step 3:

[0139] The terminal provides an input interface that accepts the type, quantity, and priority of supplies the user needs. The user uses this interface to input the information.

[0140] Step 4:

[0141] The server receives user input data and uses a generative artificial intelligence model to generate the optimal delivery route. This route takes into account the acquired data and the importance of the user.

[0142] Step 5:

[0143] The terminal displays the generated delivery route to the user, presenting it in a visualized map format for easy confirmation.

[0144] Step 6:

[0145] The device monitors the user's emotional state through an emotion engine. It analyzes the user's emotions towards the delivery plan through voice and facial recognition.

[0146] Step 7:

[0147] The emotional data analyzed by the emotion engine is sent to the server in real time. The server uses this emotional data to re-evaluate delivery routes and priorities.

[0148] Step 8:

[0149] The server dynamically adjusts the delivery route based on the user's emotional state, thereby presenting a delivery plan that is more satisfying to the user.

[0150] Step 9:

[0151] The terminal notifies the user again of the adjusted delivery route and provides an interface for final confirmation and approval. This allows for user feedback before implementation, enabling further adjustments.

[0152] (Example 2)

[0153] 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".

[0154] While conventional material delivery systems could determine efficient delivery routes based on traffic and weather data, they could not implement flexible delivery plans that considered user emotional satisfaction. This made it difficult to respond quickly to user dissatisfaction and requests, resulting in a challenge in providing an optimal user experience.

[0155] 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.

[0156] In this invention, the server includes means for integrating traffic conditions, weather conditions, and inventory status of goods into a data set; means for analyzing the data acquired at a given time and setting delivery priority orders; and means for creating an optimal goods delivery route using an intelligent processing model. This enables flexible delivery planning that takes into account not only efficiency but also the emotional satisfaction of the user.

[0157] "Traffic conditions" refers to information that indicates the flow and congestion of vehicles on the road.

[0158] "Weather conditions" refers to information that indicates the current or predicted weather conditions, and includes elements such as temperature, humidity, precipitation, and wind speed.

[0159] "Inventory status of goods" refers to information indicating the current quantity of a particular item in storage.

[0160] "Integrating data into a set" is the process of gathering and managing data obtained from different sources in one place.

[0161] "Analysis" is the process of thoroughly investigating and examining collected data to derive meaningful information.

[0162] "Setting delivery priority order" is the process of determining which of multiple delivery tasks should be executed first.

[0163] An "intelligent processing model" is a computational model that identifies patterns and relationships based on large amounts of data and autonomously seeks and presents the optimal solution.

[0164] "Creating a goods delivery route" is the process of designing a route for efficiently transporting goods from their origin to their destination.

[0165] In order to implement this invention, it is necessary to optimize the logistics system through a series of operations between the server, terminal, and user.

[0166] The server utilizes various databases and APIs to comprehensively manage traffic conditions, weather conditions, and inventory status of goods. Specifically, it uses a map information API to obtain traffic information and a weather forecast API to obtain weather data. Inventory information of goods is appropriately retrieved from warehouse management systems and other sources. The server analyzes this data using Python's Pandas and NumPy to determine delivery priorities. For generative artificial intelligence models, it utilizes TENSORFLOW® and PyTorch to generate the optimal delivery route based on prompt statements.

[0167] The terminal provides an interface with the user and evaluates the user's emotional state through video and audio input. For this purpose, it uses general-purpose speech recognition software as its speech recognition engine, and a camera and the OpenCV library for facial expression analysis. The terminal detects the user's tone and facial expressions, collects emotional data, and sends it to the server.

[0168] Users can check their delivery schedule through the terminal interface and request adjustments as needed. For example, if delivery delays are expected due to heavy rain, users can check the situation via the terminal and express their dissatisfaction or requests verbally or through facial expressions.

[0169] For example, if a route requiring a detour due to heavy rain is presented, the prompt "Please propose the optimal delivery route considering current traffic congestion information and weather forecasts" is entered into the generating AI model. The server then calculates a new route and presents the result to the user via the terminal. This process enables efficient delivery while prioritizing user satisfaction.

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] The server collects data on traffic conditions, weather conditions, and inventory status of goods via APIs and database interfaces. Inputs are information obtained from various sensors and data provision services, and output is a well-organized dataset. For data processing, the server uses Python's Pandas library to integrate the data, impute missing values, and standardize the format.

[0173] Step 2:

[0174] The server analyzes the integrated dataset to determine delivery priorities. The input is the integrated dataset obtained in step 1, and the output is a prioritized delivery list. For data calculations, NumPy is used to score the impact of traffic congestion and weather, and then calculate the delivery priority accordingly.

[0175] Step 3:

[0176] The server uses a generative artificial intelligence model to generate the optimal goods delivery route. The input is the prioritized delivery list determined in step 2 and prompts for the generative AI model, and the output is the optimized delivery route. The server uses TensorFlow to generate the route from the prompts. Specifically, the input is a prompt such as "Please suggest a route that goes through major cities and avoids traffic congestion."

[0177] Step 4:

[0178] The terminal displays the generated delivery route on a map and prompts the user for confirmation. The input is the optimized delivery route obtained in step 3, and the output is visually displayed geographical information. This operation uses map display software to show the route in a way that is easy for the user to understand.

[0179] Step 5:

[0180] The device evaluates the user's emotional state through speech recognition and facial expression analysis. Input consists of the user's spoken words and facial expression data, while output is numerical data representing the user's emotional state. Google Speech-to-Text and OpenCV are used for emotion analysis, and the device monitors changes in the user's emotions in real time.

[0181] Step 6:

[0182] Users communicate their feedback and requests regarding the displayed delivery route to their device. This feedback is ultimately sent from the device to the server as sentiment data.

[0183] Step 7:

[0184] The server re-evaluates the delivery route based on sentiment data and readjusts the route as needed. The input is the user sentiment data obtained in step 6, and the output is the updated delivery route. For readjustment, the AI ​​model is reused to recalculate a flexible route that takes user emotions into account.

[0185] (Application Example 2)

[0186] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0187] Traditional material delivery systems, while optimizing using traffic, weather, and inventory information, have a problem in that they do not take into account the emotional state of the user and therefore do not sufficiently improve user satisfaction. Furthermore, because user dissatisfaction and discomfort are not reflected in the delivery plan, there is a risk of a decline in service quality. This is a concern as it could hinder the improvement of the customer experience.

[0188] 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.

[0189] In this invention, the server includes means for integrating traffic information, weather data, and material inventory information into a data storage facility; means for analyzing data acquired in real time and determining delivery priorities; means for generating an optimal material delivery route using an artificial intelligence structure; means for evaluating the user's emotional state using an emotion analysis engine; and means for readjusting the delivery route based on the user's emotional state. This enables flexible delivery planning that considers not only technical efficiency but also the user's emotional satisfaction, thereby improving the quality of service.

[0190] "Traffic information" refers to data on road conditions, traffic conditions, congestion, and traffic regulations.

[0191] "Weather data" refers to information that specifically describes meteorological conditions, such as precipitation, temperature, wind speed, and visibility.

[0192] "Information regarding the inventory of goods" refers to data on the quantity and condition of goods and items at distribution centers and stores.

[0193] A "data storage facility" refers to databases and servers used to integrate and store information.

[0194] "Artificial intelligence structure" refers to a system based on algorithms and models designed for the purpose of solving a specific problem.

[0195] The term "emotion analysis engine" refers to a technological foundation for evaluating a user's emotional state by performing speech recognition, facial expression analysis, and contextual analysis.

[0196] "User emotional state" refers to the mental reactions and feedback exhibited by individual users, and represents a state in which satisfaction and dissatisfaction levels can be quantified.

[0197] "Means of readjusting delivery routes" refers to processes or systems for dynamically changing existing route plans based on collected data.

[0198] To implement this invention, it is necessary to construct a system in which a server and a terminal work in cooperation. First, the server integrates traffic information, weather data, and material inventory information into a data storage facility and analyzes this data in real time. Based on the analysis results, the server determines delivery priorities and then generates the optimal material delivery route using an artificial intelligence structure that it generates. This generated route information is provided to the terminal through a display device.

[0199] The device is equipped with an emotion analysis engine that evaluates the user's emotional state through speech recognition, facial expression analysis, and contextual analysis. This allows it to detect how the user is emotionally responding to the delivery plan and send that data to the server. Based on the received data on the user's emotional state, the server readjusts the delivery route as needed, providing a flexible delivery plan.

[0200] The hardware used includes server equipment and smartphones or tablet devices used by users, while the software includes EmotionAnalyzer for analyzing emotions and DeliveryRouteOptimizer for optimizing delivery routes.

[0201] For example, if a delivery may be delayed due to heavy rain, and the terminal's emotion engine detects user dissatisfaction, the server will respond quickly and suggest an alternative route to mitigate user dissatisfaction.

[0202] An example of a prompt for a generative AI model is: "Analyze the user's emotions and suggest adjustments to the delivery route. If the user is unhappy due to heavy rain, what is the best route?"

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] The server collects traffic information, weather data, and material inventory information from external data sources. This information is integrated into a data storage facility. Inputs are various data obtained from external APIs and stored in a database, enabling real-time access. Output is an integrated dataset.

[0206] Step 2:

[0207] The server analyzes and processes the integrated data to determine delivery priorities. The input is an integrated dataset, and the output is delivery priority information obtained as a result of the analysis. During the analysis process, specific algorithms are used to predict traffic congestion and sudden weather changes.

[0208] Step 3:

[0209] The server uses a generated artificial intelligence structure to calculate the optimal supply delivery route. This process optimizes routes based on priority. Inputs are delivery priority information and current traffic and weather data, and output is the optimized delivery route. The generated AI model assists in effective route calculation.

[0210] Step 4:

[0211] The server transmits calculated delivery route information to the terminal. The terminal is equipped with a display device that visually presents the delivery plan to the user. Specifically, the display includes the route and estimated travel time clearly shown on a map. The input is the delivery route data sent from the server, and the output is the information displayed on the terminal's user interface.

[0212] Step 5:

[0213] The device uses an emotion analysis engine to evaluate the user's emotional state from their voice and facial expressions. Input is the user's voice tone and facial expression information, while output is the user's emotional state data obtained through analysis. It integrates cutting-edge voice analysis and image processing technologies to measure user responses in real time.

[0214] Step 6:

[0215] The user's emotional state data is sent from the terminal to the server. The server readjusts the delivery route based on this data. The inputs here are the user's emotional state data and the current delivery route, and the output is the readjusted delivery route. If emotional dissatisfaction is detected, the server will suggest alternative routes or adjustment plans.

[0216] Step 7:

[0217] Users review the newly adjusted delivery plan through their terminal. This improves the service experience by allowing users to receive flexible responses tailored to their satisfaction and circumstances. The input is the re-adjusted delivery route from the server, and the output is the presentation of the new delivery plan to the user.

[0218] Through these steps, efficient material delivery is achieved while taking user emotions into consideration.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] [Second Embodiment]

[0223] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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".

[0235] This invention is a system that collects and integrates traffic information, weather data, and material inventory information in real time to enable effective material distribution. This system has a three-tiered structure consisting of a server, terminals, and users, each playing a different role.

[0236] The server forms the core of this system, collecting and analyzing data. It acquires real-time data from traffic information services, weather forecasting services, and warehouse management systems, and integrates it. Using this integrated data, the system can quickly set delivery priorities for goods. Furthermore, the server utilizes a generative AI model to automatically generate optimal delivery routes that take into account traffic congestion and weather conditions.

[0237] The terminal functions as a user interface, providing a means for the user to input necessary information. Through the terminal, the user can input the type, quantity, and priority of necessary supplies. The terminal transmits the input information to the server and visually displays the generated delivery plan.

[0238] Users can view the delivery plan provided by the server through their terminal and provide feedback as needed. The server uses this feedback to dynamically adjust delivery routes and priorities, increasing the flexibility of the plan.

[0239] As a concrete example, consider a scenario involving a large-scale natural disaster. The server acquires traffic and weather data from the affected area and generates the optimal delivery route based on the priority of supplies. In doing so, it takes into account road closures and severe weather conditions, while ensuring priority delivery to evacuation centers that are short on supplies. The terminal presents this delivery plan to the user, and the server flexibly modifies the plan based on additional requests and modification instructions from the user.

[0240] This system is expected to enable the rapid and effective delivery of supplies during disasters.

[0241] The following describes the processing flow.

[0242] Step 1:

[0243] The server retrieves traffic information, weather data, and material inventory information in real time from multiple external data sources. This includes obtaining information from traffic APIs and weather APIs.

[0244] Step 2:

[0245] The server centrally integrates the acquired data, formats it, and organizes it into a consistent database. This eliminates duplicate data and standardizes the format.

[0246] Step 3:

[0247] Users input the type, quantity, and priority of necessary supplies via a terminal. This information is sent to the server as basic data for the supply delivery plan.

[0248] Step 4:

[0249] The server analyzes real-time traffic and weather data to determine delivery priorities. This includes analysis that takes into account congestion and weather conditions.

[0250] Step 5:

[0251] The server uses a generative artificial intelligence model to generate the optimal delivery route. This AI model calculates the route that maximizes delivery efficiency based on the acquired data.

[0252] Step 6:

[0253] The terminal receives the delivery route generated from the server and displays it in the user interface. Users can view visualized maps and detailed lists.

[0254] Step 7:

[0255] Users can check delivery routes via their devices and send feedback or change requests to the server as needed. This feedback can include specific delivery needs and changes.

[0256] Step 8:

[0257] The server receives user feedback, re-evaluates delivery routes, and dynamically adjusts them as needed. This enables flexible and adaptive delivery planning.

[0258] Step 9:

[0259] The terminal then provides the user with the updated delivery plan again and supports final confirmation and approval. This ensures reliable delivery of goods.

[0260] (Example 1)

[0261] 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."

[0262] There is a need to respond quickly to changes in traffic conditions, weather conditions, and fluctuations in material inventory, and to deliver goods efficiently. However, conventional systems have difficulty integrating this diverse information in real time, making it difficult to flexibly generate and adjust optimal delivery routes. Furthermore, there is a lack of mechanisms to appropriately revise plans based on dynamic feedback from users, resulting in problems with effective material delivery.

[0263] 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.

[0264] In this invention, the server includes means for integrating traffic information, weather data, and inventory data into a data set; means for analyzing information acquired in real time and determining delivery priorities; and means for generating an optimal transportation route based on prompt messages using a generative artificial intelligence model. This makes it possible to formulate an optimal delivery plan based on information that changes in real time and to flexibly adjust the plan by utilizing user feedback.

[0265] "Traffic information" refers to information that shows the flow of traffic, congestion levels, and passable routes.

[0266] "Weather data" refers to information related to weather, such as weather conditions, forecasts, temperature, and precipitation.

[0267] "Inventory data" refers to management information such as the quantity and location of goods.

[0268] A "data set" is a collection of information that integrates multiple data points obtained from different sources.

[0269] "Real-time" refers to a state where the time between information acquisition and processing is instantaneous.

[0270] A "generative artificial intelligence model" is a computer program that implements algorithms for making predictions and optimizations based on input data.

[0271] A "prompt" is a text that describes instructions or questions given to a generative artificial intelligence model in order to produce an appropriate output.

[0272] A "transportation route" refers to a specific path or route planned for the efficient movement of goods.

[0273] This invention is a system that integrates traffic information, weather data, and inventory data in real time to achieve optimal material delivery. This system mainly consists of three elements: a server, terminals, and users.

[0274] The server functions as the central hub of this system, collecting information from diverse data sources. Specifically, it utilizes traffic information services to understand traffic conditions, weather forecasting services to obtain weather predictions, and a warehouse management system to manage inventory levels. This data is integrated into the server and analyzed immediately. A generative AI model is used for analysis, and appropriate transportation routes are calculated based on the input of prompts. An example of a prompt is, "Consider the traffic and weather data for the specified area, and create the optimal supply delivery route to facilities experiencing shortages."

[0275] The terminal serves as the user interface. Through the terminal, users can input necessary material information and verify the generated transportation route. Furthermore, the terminal also acts as a medium for sending user feedback to the server.

[0276] Users can participate in the system in real time by using these terminals to efficiently manage supplies, check delivery plans, and provide feedback as needed. This participation enables the system to operate quickly and flexibly.

[0277] A concrete example is the emergency delivery of supplies during a disaster. Users input information about the supplies they need immediately into their terminals, and then review and adjust the delivery routes generated based on the latest traffic and weather data provided by the server. This enables the most effective delivery plan and allows for the immediate provision of necessary assistance.

[0278] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0279] Step 1:

[0280] The server obtains data from a traffic information providing service, a weather prediction service, and a warehouse management system. The input is raw data obtained through API requests from these services. This collects real-time data on traffic conditions, weather conditions, and inventory levels. These data are integrated within a database.

[0281] Step 2:

[0282] The server analyzes the data it has obtained and determines the delivery priority of supplies. The input is the data integrated in Step 1. This data is analyzed and priorities are assigned based on specific conditions (e.g., stock shortage, urgency). The output is a dataset with set priorities.

[0283] Step 3:

[0284] The server inputs a prompt sentence into the generated artificial intelligence model to generate an optimal transportation route. The input is the prioritized data obtained in Step 2 and the prompt sentence "Please create an optimal supply delivery route to facilities with supply shortages considering the traffic information and weather data of the specified area." By using the generated AI model, an optimal route considering traffic congestion and weather is output.

[0285] Step 4:

[0286] The terminal visually displays the generated transportation route to the user. The input is the transportation route generated by the server in Step 3. By visualizing this on a map, it enables the user to easily understand the transportation plan. The output is a detailed transportation route presented to the user.

[0287] Step 5:

[0288] Users provide feedback through their devices, and the delivery route is adjusted as needed. The input consists of the user's opinions and requests regarding the delivery plan presented in step 4. The output is sent to the server as a revised request and reflected in the next plan. This improves the flexibility of the delivery plan.

[0289] (Application Example 1)

[0290] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0291] To achieve rapid and efficient delivery of goods at logistics facilities, it is necessary to quickly generate and dynamically adjust optimal delivery routes that take into account traffic information and weather conditions. However, current technology does not adequately modify or optimize delivery plans in real time, leading to problems such as reduced delivery efficiency and delays. Furthermore, it is difficult to immediately incorporate feedback from delivery personnel, and the inability to flexibly adjust routes is a challenge.

[0292] 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.

[0293] In this invention, the server includes means for integrating traffic information, weather data, and inventory information of goods into an information recording device; means for analyzing the information acquired in real time and determining the delivery order; and means for generating the optimal material delivery route using a generative artificial intelligence model. This enables logistics facility managers and transportation personnel to obtain the optimal delivery route in real time and improve delivery efficiency. Furthermore, a feedback function using a communication terminal allows for dynamic modification of the delivery plan and maintenance of the optimal route.

[0294] "Traffic information" refers to real-time data on transportation conditions, such as road congestion and traffic restrictions.

[0295] "Weather data" refers to real-time numerical information about weather conditions, such as weather, temperature, precipitation, and wind speed.

[0296] "Goods inventory information" refers to data regarding the quantity and condition of goods in warehouses and logistics facilities.

[0297] An "information recording device" is a computer system or database used to integrate and store various types of data.

[0298] "Analysis" is the process of processing acquired information to derive meaningful results from data.

[0299] "Delivery order" refers to the criteria used to determine the priority and order in which goods are delivered.

[0300] A "generative artificial intelligence model" is an algorithm or program that uses machine learning to generate the optimal delivery route.

[0301] A "delivery route" is a plan that indicates the optimal route for delivering goods.

[0302] A "display device" is a device, such as a computer or smartphone, that visually displays information.

[0303] A "communication terminal" is a portable information processing device capable of data communication, such as a smartphone or tablet.

[0304] "Real-time feedback" is a communication function that instantly receives and reflects information from the sender.

[0305] "Dynamic reconstruction" refers to the process of updating and modifying existing plans and structures in real time based on new information from external sources.

[0306] The system that realizes this invention mainly consists of three components: a server, a terminal, and a user.

[0307] The server aggregates traffic information, weather data, inventory information of goods, etc. in real time and integrates them into an information recording device. At this time, external services such as Google Maps API and weather data API are used to collect information. The collected data is analyzed using Python scripts, and the generated artificial intelligence model generates an optimal delivery route. In this generation process, optimization calculations considering traffic conditions and weather conditions are performed, and the generated delivery route is provided to the terminal in real time.

[0308] The terminal is a smartphone or tablet used by logistics facility managers and transportation personnel. On the terminal, the generated delivery plan is graphically displayed, and each user can plan their actions based on this. It also has a real-time feedback function, and by providing the delivery status and new information to the server, dynamic modification of the delivery plan is made possible.

[0309] Users check the delivery route and priorities through the terminal and provide feedback as needed. The feedback is sent to the server, and the delivery plan is automatically readjusted. With this function, it is possible to respond flexibly and immediately even when sudden changes in weather or unexpected traffic jams occur.

[0310] As a specific example, consider the case where a logistics facility efficiently handles the delivery peak during the Christmas season. With this system, it is possible to optimize the route while minimizing delays considering deteriorating weather and traffic conditions.

[0311] Examples of prompt texts for the generation AI model include "Based on the current traffic data and weather conditions, please generate an optimal route to the following delivery destinations. Please consider shortening the arrival time in particularly bad weather." In response to this prompt, the AI proposes an optimal route.

[0312] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0313] Step 1:

[0314] The server collects traffic information, weather data, and inventory information in real time using the Google Maps API and weather data API. This information is integrated into an information recording device. As input, data from each API is retrieved and stored in a database. As output, an integrated dataset is prepared.

[0315] Step 2:

[0316] The server analyzes the integrated dataset using a Python script to determine the delivery order. The input is the integrated dataset from step 1, and the priority and necessity of each data point are evaluated. The output is a list of prioritized delivery tasks.

[0317] Step 3:

[0318] The server uses a generated artificial intelligence model to create the optimal delivery route using prompts. The inputs here are the prompts and the list of delivery tasks obtained in step 2. The generated AI model calculates the optimal route, and the output is a specific delivery route.

[0319] Step 4:

[0320] The server sends the generated delivery route to the terminal, which then displays it visually on the display device. The input is the delivery route generated in step 3, and the output is the plan displayed on the screen of a terminal used by logistics facility managers or transportation personnel.

[0321] Step 5:

[0322] Users can view delivery plans via their devices and provide real-time feedback as needed. Input is the displayed delivery route, and users provide feedback. Output is the user's feedback information sent to the server.

[0323] Step 6:

[0324] The server readjusts the delivery plan based on user feedback. The input here is the feedback information obtained in step 5. Through data processing, the AI ​​recalculates, and the output is an updated delivery route. This updated information is sent back to the terminal, ensuring that the optimal route is always maintained.

[0325] 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.

[0326] This invention combines a system that utilizes traffic information, weather data, and inventory information to achieve optimal material delivery with an emotion engine. The emotion engine recognizes the user's emotional state and uses this to adjust the delivery plan.

[0327] The server collects and integrates traffic information, weather data, and material inventory information as before. It performs real-time analysis of this data to determine delivery priorities. Furthermore, it can use generative artificial intelligence models to generate optimal material delivery routes based on the existing information.

[0328] The terminal is responsible for the user interface and is equipped with an emotion engine. This emotion engine evaluates the user's emotions through speech recognition, facial expression analysis, and contextual analysis. This makes it possible to quantify how the user is reacting to the delivery plan and the degree of dissatisfaction or satisfaction.

[0329] The user enters delivery plan details using a terminal and confirms the visually displayed delivery route. Simultaneously, an emotion engine analyzes the user's tone of voice and facial expressions and sends this emotion data to the server. Based on this emotion data, the server readjusts the delivery route as needed. This enables flexible delivery planning that considers not only efficiency but also the user's emotional satisfaction.

[0330] As a concrete example, suppose a user receives a delivery route that requires a detour due to heavy rain. The terminal detects the user's dissatisfaction with this using an emotion engine and transmits it to the server. The server considers this emotion data, reconsiders the route, changes priorities, and provides a plan that satisfies the user. In this way, this system combines technical optimization with improved user experience.

[0331] The following describes the processing flow.

[0332] Step 1:

[0333] The server acquires and integrates real-time traffic information, weather data, and inventory information from external sources. This creates a database based on the latest road and weather conditions.

[0334] Step 2:

[0335] The server analyzes the integrated data to determine priorities for efficient supply delivery. This analysis includes decisions that take into account weather conditions and the presence or absence of traffic disruptions.

[0336] Step 3:

[0337] The terminal provides an input interface that accepts the type, quantity, and priority of supplies the user needs. The user uses this interface to input the information.

[0338] Step 4:

[0339] The server receives user input data and uses a generative artificial intelligence model to generate the optimal delivery route. This route takes into account the acquired data and the importance of the user.

[0340] Step 5:

[0341] The terminal displays the generated delivery route to the user, presenting it in a visualized map format for easy confirmation.

[0342] Step 6:

[0343] The device monitors the user's emotional state through an emotion engine. It analyzes the user's emotions towards the delivery plan through voice and facial recognition.

[0344] Step 7:

[0345] The emotional data analyzed by the emotion engine is sent to the server in real time. The server uses this emotional data to re-evaluate delivery routes and priorities.

[0346] Step 8:

[0347] The server dynamically adjusts the delivery route based on the user's emotional state, thereby presenting a delivery plan that is more satisfying to the user.

[0348] Step 9:

[0349] The terminal notifies the user again of the adjusted delivery route and provides an interface for final confirmation and approval. This allows for user feedback before implementation, enabling further adjustments.

[0350] (Example 2)

[0351] 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".

[0352] While conventional material delivery systems could determine efficient delivery routes based on traffic and weather data, they could not implement flexible delivery plans that considered user emotional satisfaction. This made it difficult to respond quickly to user dissatisfaction and requests, resulting in a challenge in providing an optimal user experience.

[0353] 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.

[0354] In this invention, the server includes means for integrating traffic conditions, weather conditions, and inventory status of goods into a data set; means for analyzing the data acquired at a given time and setting delivery priority orders; and means for creating an optimal goods delivery route using an intelligent processing model. This enables flexible delivery planning that takes into account not only efficiency but also the emotional satisfaction of the user.

[0355] "Traffic conditions" refers to information that indicates the flow and congestion of vehicles on the road.

[0356] "Weather conditions" refers to information that indicates the current or predicted weather conditions, and includes elements such as temperature, humidity, precipitation, and wind speed.

[0357] "Inventory status of goods" refers to information indicating the current quantity of a particular item in storage.

[0358] "Integrating data into a set" is the process of gathering and managing data obtained from different sources in one place.

[0359] "Analysis" is the process of thoroughly investigating and examining collected data to derive meaningful information.

[0360] "Setting delivery priority order" is the process of determining which of multiple delivery tasks should be executed first.

[0361] An "intelligent processing model" is a computational model that identifies patterns and relationships based on large amounts of data and autonomously seeks and presents the optimal solution.

[0362] "Creating a goods delivery route" is the process of designing a route for efficiently transporting goods from their origin to their destination.

[0363] In order to implement this invention, it is necessary to optimize the logistics system through a series of operations between the server, terminal, and user.

[0364] The server utilizes various databases and APIs to comprehensively manage traffic conditions, weather conditions, and inventory status of goods. Specifically, it uses a map information API to obtain traffic information and a weather forecast API to obtain weather data. Inventory information is appropriately retrieved from warehouse management systems and other sources. The server analyzes this data using Python's Pandas and NumPy to determine delivery priorities. TensorFlow and PyTorch are used in the generative artificial intelligence model to generate the optimal delivery route based on prompt messages.

[0365] The terminal provides an interface with the user and evaluates the user's emotional state through video and audio input. For this purpose, it uses general-purpose speech recognition software as its speech recognition engine, and a camera and the OpenCV library for facial expression analysis. The terminal detects the user's tone and facial expressions, collects emotional data, and sends it to the server.

[0366] Users can check their delivery schedule through the terminal interface and request adjustments as needed. For example, if delivery delays are expected due to heavy rain, users can check the situation via the terminal and express their dissatisfaction or requests verbally or through facial expressions.

[0367] For example, if a route requiring a detour due to heavy rain is presented, the prompt "Please propose the optimal delivery route considering current traffic congestion information and weather forecasts" is entered into the generating AI model. The server then calculates a new route and presents the result to the user via the terminal. This process enables efficient delivery while prioritizing user satisfaction.

[0368] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0369] Step 1:

[0370] The server collects data on traffic conditions, weather conditions, and inventory status of goods via APIs and database interfaces. Inputs are information obtained from various sensors and data provision services, and output is a well-organized dataset. For data processing, the server uses Python's Pandas library to integrate the data, impute missing values, and standardize the format.

[0371] Step 2:

[0372] The server analyzes the integrated dataset to determine delivery priorities. The input is the integrated dataset obtained in step 1, and the output is a prioritized delivery list. For data calculations, NumPy is used to score the impact of traffic congestion and weather, and then calculate the delivery priority accordingly.

[0373] Step 3:

[0374] The server uses a generative artificial intelligence model to generate the optimal goods delivery route. The input is the prioritized delivery list determined in step 2 and prompts for the generative AI model, and the output is the optimized delivery route. The server uses TensorFlow to generate the route from the prompts. Specifically, the input is a prompt such as "Please suggest a route that goes through major cities and avoids traffic congestion."

[0375] Step 4:

[0376] The terminal displays the generated delivery route on a map and prompts the user for confirmation. The input is the optimized delivery route obtained in step 3, and the output is visually displayed geographical information. This operation uses map display software to show the route in a way that is easy for the user to understand.

[0377] Step 5:

[0378] The device evaluates the user's emotional state through speech recognition and facial expression analysis. Input consists of the user's spoken words and facial expression data, while output is numerical data representing the user's emotional state. Google Speech-to-Text and OpenCV are used for emotion analysis, and the device monitors changes in the user's emotions in real time.

[0379] Step 6:

[0380] Users communicate their feedback and requests regarding the displayed delivery route to their device. This feedback is ultimately sent from the device to the server as sentiment data.

[0381] Step 7:

[0382] The server re-evaluates the delivery route based on sentiment data and readjusts the route as needed. The input is the user sentiment data obtained in step 6, and the output is the updated delivery route. For readjustment, the AI ​​model is reused to recalculate a flexible route that takes user emotions into account.

[0383] (Application Example 2)

[0384] 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."

[0385] Traditional material delivery systems, while optimizing using traffic, weather, and inventory information, have a problem in that they do not take into account the emotional state of the user and therefore do not sufficiently improve user satisfaction. Furthermore, because user dissatisfaction and discomfort are not reflected in the delivery plan, there is a risk of a decline in service quality. This is a concern as it could hinder the improvement of the customer experience.

[0386] 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.

[0387] In this invention, the server includes means for integrating traffic information, weather data, and material inventory information into a data storage facility; means for analyzing data acquired in real time and determining delivery priorities; means for generating an optimal material delivery route using an artificial intelligence structure; means for evaluating the user's emotional state using an emotion analysis engine; and means for readjusting the delivery route based on the user's emotional state. This enables flexible delivery planning that considers not only technical efficiency but also the user's emotional satisfaction, thereby improving the quality of service.

[0388] "Traffic information" refers to data on road conditions, traffic conditions, congestion, and traffic regulations.

[0389] "Weather data" refers to information that specifically describes meteorological conditions, such as precipitation, temperature, wind speed, and visibility.

[0390] "Information regarding the inventory of goods" refers to data on the quantity and condition of goods and items at distribution centers and stores.

[0391] A "data storage facility" refers to databases and servers used to integrate and store information.

[0392] "Artificial intelligence structure" refers to a system based on algorithms and models designed for the purpose of solving a specific problem.

[0393] The term "emotion analysis engine" refers to a technological foundation for evaluating a user's emotional state by performing speech recognition, facial expression analysis, and contextual analysis.

[0394] "User emotional state" refers to the mental reactions and feedback exhibited by individual users, and represents a state in which satisfaction and dissatisfaction levels can be quantified.

[0395] "Means of readjusting delivery routes" refers to processes or systems for dynamically changing existing route plans based on collected data.

[0396] To implement this invention, it is necessary to construct a system in which a server and a terminal work in cooperation. First, the server integrates traffic information, weather data, and material inventory information into a data storage facility and analyzes this data in real time. Based on the analysis results, the server determines delivery priorities and then generates the optimal material delivery route using an artificial intelligence structure that it generates. This generated route information is provided to the terminal through a display device.

[0397] The device is equipped with an emotion analysis engine that evaluates the user's emotional state through speech recognition, facial expression analysis, and contextual analysis. This allows it to detect how the user is emotionally responding to the delivery plan and send that data to the server. Based on the received data on the user's emotional state, the server readjusts the delivery route as needed, providing a flexible delivery plan.

[0398] The hardware used includes server equipment and smartphones or tablet devices used by users, while the software includes EmotionAnalyzer for analyzing emotions and DeliveryRouteOptimizer for optimizing delivery routes.

[0399] For example, if a delivery may be delayed due to heavy rain, and the terminal's emotion engine detects user dissatisfaction, the server will respond quickly and suggest an alternative route to mitigate user dissatisfaction.

[0400] An example of a prompt for a generative AI model is: "Analyze the user's emotions and suggest adjustments to the delivery route. If the user is unhappy due to heavy rain, what is the best route?"

[0401] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0402] Step 1:

[0403] The server collects traffic information, weather data, and material inventory information from external data sources. This information is integrated into a data storage facility. Inputs are various data obtained from external APIs and stored in a database, enabling real-time access. Output is an integrated dataset.

[0404] Step 2:

[0405] The server analyzes and processes the integrated data to determine delivery priorities. The input is an integrated dataset, and the output is delivery priority information obtained as a result of the analysis. During the analysis process, specific algorithms are used to predict traffic congestion and sudden weather changes.

[0406] Step 3:

[0407] The server uses a generated artificial intelligence structure to calculate the optimal supply delivery route. This process optimizes routes based on priority. Inputs are delivery priority information and current traffic and weather data, and output is the optimized delivery route. The generated AI model assists in effective route calculation.

[0408] Step 4:

[0409] The server transmits calculated delivery route information to the terminal. The terminal is equipped with a display device that visually presents the delivery plan to the user. Specifically, the display includes the route and estimated travel time clearly shown on a map. The input is the delivery route data sent from the server, and the output is the information displayed on the terminal's user interface.

[0410] Step 5:

[0411] The device uses an emotion analysis engine to evaluate the user's emotional state from their voice and facial expressions. Input is the user's voice tone and facial expression information, while output is the user's emotional state data obtained through analysis. It integrates cutting-edge voice analysis and image processing technologies to measure user responses in real time.

[0412] Step 6:

[0413] The user's emotional state data is sent from the terminal to the server. The server readjusts the delivery route based on this data. The inputs here are the user's emotional state data and the current delivery route, and the output is the readjusted delivery route. If emotional dissatisfaction is detected, the server will suggest alternative routes or adjustment plans.

[0414] Step 7:

[0415] Users review the newly adjusted delivery plan through their terminal. This improves the service experience by allowing users to receive flexible responses tailored to their satisfaction and circumstances. The input is the re-adjusted delivery route from the server, and the output is the presentation of the new delivery plan to the user.

[0416] Through these steps, efficient material delivery is achieved while taking user emotions into consideration.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] [Third Embodiment]

[0421] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0422] 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.

[0423] 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).

[0424] 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.

[0425] 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.

[0426] 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).

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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".

[0433] This invention is a system that collects and integrates traffic information, weather data, and material inventory information in real time to enable effective material distribution. This system has a three-tiered structure consisting of a server, terminals, and users, each playing a different role.

[0434] The server forms the core of this system, collecting and analyzing data. It acquires real-time data from traffic information services, weather forecasting services, and warehouse management systems, and integrates it. Using this integrated data, the system can quickly set delivery priorities for goods. Furthermore, the server utilizes a generative AI model to automatically generate optimal delivery routes that take into account traffic congestion and weather conditions.

[0435] The terminal functions as a user interface, providing a means for the user to input necessary information. Through the terminal, the user can input the type, quantity, and priority of necessary supplies. The terminal transmits the input information to the server and visually displays the generated delivery plan.

[0436] Users can view the delivery plan provided by the server through their terminal and provide feedback as needed. The server uses this feedback to dynamically adjust delivery routes and priorities, increasing the flexibility of the plan.

[0437] As a concrete example, consider a scenario involving a large-scale natural disaster. The server acquires traffic and weather data from the affected area and generates the optimal delivery route based on the priority of supplies. In doing so, it takes into account road closures and severe weather conditions, while ensuring priority delivery to evacuation centers that are short on supplies. The terminal presents this delivery plan to the user, and the server flexibly modifies the plan based on additional requests and modification instructions from the user.

[0438] This system is expected to enable the rapid and effective delivery of supplies during disasters.

[0439] The following describes the processing flow.

[0440] Step 1:

[0441] The server retrieves traffic information, weather data, and material inventory information in real time from multiple external data sources. This includes obtaining information from traffic APIs and weather APIs.

[0442] Step 2:

[0443] The server centrally integrates the acquired data, formats it, and organizes it into a consistent database. This eliminates duplicate data and standardizes the format.

[0444] Step 3:

[0445] Users input the type, quantity, and priority of necessary supplies via a terminal. This information is sent to the server as basic data for the supply delivery plan.

[0446] Step 4:

[0447] The server analyzes real-time traffic and weather data to determine delivery priorities. This includes analysis that takes into account congestion and weather conditions.

[0448] Step 5:

[0449] The server uses a generative artificial intelligence model to generate the optimal delivery route. This AI model calculates the route that maximizes delivery efficiency based on the acquired data.

[0450] Step 6:

[0451] The terminal receives the delivery route generated from the server and displays it in the user interface. Users can view visualized maps and detailed lists.

[0452] Step 7:

[0453] Users can check delivery routes via their devices and send feedback or change requests to the server as needed. This feedback can include specific delivery needs and changes.

[0454] Step 8:

[0455] The server receives user feedback, re-evaluates delivery routes, and dynamically adjusts them as needed. This enables flexible and adaptive delivery planning.

[0456] Step 9:

[0457] The terminal then provides the user with the updated delivery plan again and supports final confirmation and approval. This ensures reliable delivery of goods.

[0458] (Example 1)

[0459] 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."

[0460] There is a need to respond quickly to changes in traffic conditions, weather conditions, and fluctuations in material inventory, and to deliver goods efficiently. However, conventional systems have difficulty integrating this diverse information in real time, making it difficult to flexibly generate and adjust optimal delivery routes. Furthermore, there is a lack of mechanisms to appropriately revise plans based on dynamic feedback from users, resulting in problems with effective material delivery.

[0461] 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.

[0462] In this invention, the server includes means for integrating traffic information, weather data, and inventory data into a data set; means for analyzing information acquired in real time and determining delivery priorities; and means for generating an optimal transportation route based on prompt messages using a generative artificial intelligence model. This makes it possible to formulate an optimal delivery plan based on information that changes in real time and to flexibly adjust the plan by utilizing user feedback.

[0463] "Traffic information" refers to information that shows the flow of traffic, congestion levels, and passable routes.

[0464] "Weather data" refers to information related to weather, such as weather conditions, forecasts, temperature, and precipitation.

[0465] "Inventory data" refers to management information such as the quantity and location of goods.

[0466] A "data set" is a collection of information that integrates multiple data points obtained from different sources.

[0467] "Real-time" refers to a state where the time between information acquisition and processing is instantaneous.

[0468] A "generative artificial intelligence model" is a computer program that implements algorithms for making predictions and optimizations based on input data.

[0469] A "prompt" is a text that describes instructions or questions given to a generative artificial intelligence model in order to produce an appropriate output.

[0470] A "transportation route" refers to a specific path or route planned for the efficient movement of goods.

[0471] This invention is a system that integrates traffic information, weather data, and inventory data in real time to achieve optimal material delivery. This system mainly consists of three elements: a server, terminals, and users.

[0472] The server functions as the central hub of this system, collecting information from diverse data sources. Specifically, it utilizes traffic information services to understand traffic conditions, weather forecasting services to obtain weather predictions, and a warehouse management system to manage inventory levels. This data is integrated into the server and analyzed immediately. A generative AI model is used for analysis, and appropriate transportation routes are calculated based on the input of prompts. An example of a prompt is, "Consider the traffic and weather data for the specified area, and create the optimal supply delivery route to facilities experiencing shortages."

[0473] The terminal serves as the user interface. Through the terminal, users can input necessary material information and verify the generated transportation route. Furthermore, the terminal also acts as a medium for sending user feedback to the server.

[0474] Users can participate in the system in real time by using these terminals to efficiently manage supplies, check delivery plans, and provide feedback as needed. This participation enables the system to operate quickly and flexibly.

[0475] A concrete example is the emergency delivery of supplies during a disaster. Users input information about the supplies they need immediately into their terminals, and then review and adjust the delivery routes generated based on the latest traffic and weather data provided by the server. This enables the most effective delivery plan and allows for the immediate provision of necessary assistance.

[0476] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0477] Step 1:

[0478] The server retrieves data from traffic information services, weather forecasting services, and warehouse management systems. The input is raw data obtained through API requests from these services. This allows for the collection of real-time data on traffic conditions, weather conditions, and inventory levels. This data is then integrated within a database.

[0479] Step 2:

[0480] The server analyzes the acquired data to determine the priority of supply delivery. The input is the data integrated in step 1. This data is analyzed, and priorities are assigned based on specific conditions (e.g., insufficient stock, urgency). The output is a dataset with the assigned priorities.

[0481] Step 3:

[0482] The server inputs a prompt message into the generating AI model to generate the optimal transportation route. The input consists of the prioritized data obtained in step 2 and the prompt message: "Consider the traffic and weather data for the specified area and create the optimal supply delivery route to the facility experiencing shortages." Using the generating AI model, the optimal route that takes traffic congestion and weather into account is output.

[0483] Step 4:

[0484] The terminal visually displays the generated transport route to the user. The input is the transport route generated by the server in step 3. Visualizing this on a map makes it easy for the user to understand the transport plan. The output is the detailed transport route presented to the user.

[0485] Step 5:

[0486] Users provide feedback through their devices, and the delivery route is adjusted as needed. The input consists of the user's opinions and requests regarding the delivery plan presented in step 4. The output is sent to the server as a revised request and reflected in the next plan. This improves the flexibility of the delivery plan.

[0487] (Application Example 1)

[0488] 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."

[0489] To achieve rapid and efficient delivery of goods at logistics facilities, it is necessary to quickly generate and dynamically adjust optimal delivery routes that take into account traffic information and weather conditions. However, current technology does not adequately modify or optimize delivery plans in real time, leading to problems such as reduced delivery efficiency and delays. Furthermore, it is difficult to immediately incorporate feedback from delivery personnel, and the inability to flexibly adjust routes is a challenge.

[0490] 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.

[0491] In this invention, the server includes means for integrating traffic information, weather data, and inventory information of goods into an information recording device; means for analyzing the information acquired in real time and determining the delivery order; and means for generating the optimal material delivery route using a generative artificial intelligence model. This enables logistics facility managers and transportation personnel to obtain the optimal delivery route in real time and improve delivery efficiency. Furthermore, a feedback function using a communication terminal allows for dynamic modification of the delivery plan and maintenance of the optimal route.

[0492] "Traffic information" refers to real-time data on transportation conditions, such as road congestion and traffic restrictions.

[0493] "Weather data" refers to real-time numerical information about weather conditions, such as weather, temperature, precipitation, and wind speed.

[0494] "Goods inventory information" refers to data regarding the quantity and condition of goods in warehouses and logistics facilities.

[0495] An "information recording device" is a computer system or database used to integrate and store various types of data.

[0496] "Analysis" is the process of processing acquired information to derive meaningful results from data.

[0497] "Delivery order" refers to the criteria used to determine the priority and order in which goods are delivered.

[0498] A "generative artificial intelligence model" is an algorithm or program that uses machine learning to generate the optimal delivery route.

[0499] A "delivery route" is a plan that indicates the optimal route for delivering goods.

[0500] A "display device" is a device, such as a computer or smartphone, that visually displays information.

[0501] A "communication terminal" is a portable information processing device capable of data communication, such as a smartphone or tablet.

[0502] "Real-time feedback" is a communication function that instantly receives and reflects information from the sender.

[0503] "Dynamic reconstruction" refers to the process of updating and modifying existing plans and structures in real time based on new information from external sources.

[0504] The system that realizes this invention mainly consists of three components: a server, a terminal, and a user.

[0505] The server aggregates traffic information, weather data, and inventory information in real time and integrates it into an information recording device. External services such as the Google Maps API and weather data API are used to collect information. The collected data is analyzed using a Python script, and a generative artificial intelligence model generates the optimal delivery route. This generation process includes optimization calculations that take traffic and weather conditions into account, and the generated delivery route is provided to the terminal in real time.

[0506] The terminals are smartphones and tablets used by logistics facility managers and transportation personnel. The generated delivery plans are displayed graphically on the terminals, allowing each user to plan their actions based on them. Furthermore, they feature real-time feedback, enabling dynamic modifications to the delivery plan by providing delivery status and new information to the server.

[0507] Users can check delivery routes and priorities through their devices and provide feedback as needed. This feedback is sent to the server, and the delivery plan is automatically readjusted. This feature allows for flexible and immediate responses, even in cases such as sudden changes in weather or unexpected traffic congestion.

[0508] As a concrete example, consider a logistics facility efficiently handling the peak delivery period during the Christmas season. This system makes it possible to optimize routes while minimizing delays, even while taking into account worsening weather and traffic conditions.

[0509] An example of a prompt to the generating AI model would be: "Based on current traffic data and weather conditions, generate the optimal route to the following delivery destinations. Please pay particular attention to reducing estimated arrival times in bad weather." In response to this prompt, the AI ​​will propose the optimal route.

[0510] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0511] Step 1:

[0512] The server collects traffic information, weather data, and inventory information in real time using the Google Maps API and weather data API. This information is integrated into an information recording device. As input, data from each API is retrieved and stored in a database. As output, an integrated dataset is prepared.

[0513] Step 2:

[0514] The server analyzes the integrated dataset using a Python script to determine the delivery order. The input is the integrated dataset from step 1, and the priority and necessity of each data point are evaluated. The output is a list of prioritized delivery tasks.

[0515] Step 3:

[0516] The server uses a generated artificial intelligence model to create the optimal delivery route using prompts. The inputs here are the prompts and the list of delivery tasks obtained in step 2. The generated AI model calculates the optimal route, and the output is a specific delivery route.

[0517] Step 4:

[0518] The server sends the generated delivery route to the terminal, which then displays it visually on the display device. The input is the delivery route generated in step 3, and the output is the plan displayed on the screen of a terminal used by logistics facility managers or transportation personnel.

[0519] Step 5:

[0520] Users can view delivery plans via their devices and provide real-time feedback as needed. Input is the displayed delivery route, and users provide feedback. Output is the user's feedback information sent to the server.

[0521] Step 6:

[0522] The server readjusts the delivery plan based on user feedback. The input here is the feedback information obtained in step 5. Through data processing, the AI ​​recalculates, and the output is an updated delivery route. This updated information is sent back to the terminal, ensuring that the optimal route is always maintained.

[0523] 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.

[0524] This invention combines a system that utilizes traffic information, weather data, and inventory information to achieve optimal material delivery with an emotion engine. The emotion engine recognizes the user's emotional state and uses this to adjust the delivery plan.

[0525] The server collects and integrates traffic information, weather data, and material inventory information as before. It performs real-time analysis of this data to determine delivery priorities. Furthermore, it can use generative artificial intelligence models to generate optimal material delivery routes based on the existing information.

[0526] The terminal is responsible for the user interface and is equipped with an emotion engine. This emotion engine evaluates the user's emotions through speech recognition, facial expression analysis, and contextual analysis. This makes it possible to quantify how the user is reacting to the delivery plan and the degree of dissatisfaction or satisfaction.

[0527] The user enters delivery plan details using a terminal and confirms the visually displayed delivery route. Simultaneously, an emotion engine analyzes the user's tone of voice and facial expressions and sends this emotion data to the server. Based on this emotion data, the server readjusts the delivery route as needed. This enables flexible delivery planning that considers not only efficiency but also the user's emotional satisfaction.

[0528] As a concrete example, suppose a user receives a delivery route that requires a detour due to heavy rain. The terminal detects the user's dissatisfaction with this using an emotion engine and transmits it to the server. The server considers this emotion data, reconsiders the route, changes priorities, and provides a plan that satisfies the user. In this way, this system combines technical optimization with improved user experience.

[0529] The following describes the processing flow.

[0530] Step 1:

[0531] The server acquires and integrates real-time traffic information, weather data, and inventory information from external sources. This creates a database based on the latest road and weather conditions.

[0532] Step 2:

[0533] The server analyzes the integrated data to determine priorities for efficient supply delivery. This analysis includes decisions that take into account weather conditions and the presence or absence of traffic disruptions.

[0534] Step 3:

[0535] The terminal provides an input interface that accepts the type, quantity, and priority of supplies the user needs. The user uses this interface to input the information.

[0536] Step 4:

[0537] The server receives user input data and uses a generative artificial intelligence model to generate the optimal delivery route. This route takes into account the acquired data and the importance of the user.

[0538] Step 5:

[0539] The terminal displays the generated delivery route to the user, presenting it in a visualized map format for easy confirmation.

[0540] Step 6:

[0541] The device monitors the user's emotional state through an emotion engine. It analyzes the user's emotions towards the delivery plan through voice and facial recognition.

[0542] Step 7:

[0543] The emotional data analyzed by the emotion engine is sent to the server in real time. The server uses this emotional data to re-evaluate delivery routes and priorities.

[0544] Step 8:

[0545] The server dynamically adjusts the delivery route based on the user's emotional state, thereby presenting a delivery plan that is more satisfying to the user.

[0546] Step 9:

[0547] The terminal notifies the user again of the adjusted delivery route and provides an interface for final confirmation and approval. This allows for user feedback before implementation, enabling further adjustments.

[0548] (Example 2)

[0549] 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."

[0550] While conventional material delivery systems could determine efficient delivery routes based on traffic and weather data, they could not implement flexible delivery plans that considered user emotional satisfaction. This made it difficult to respond quickly to user dissatisfaction and requests, resulting in a challenge in providing an optimal user experience.

[0551] 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.

[0552] In this invention, the server includes means for integrating traffic conditions, weather conditions, and inventory status of goods into a data set; means for analyzing the data acquired at a given time and setting delivery priority orders; and means for creating an optimal goods delivery route using an intelligent processing model. This enables flexible delivery planning that takes into account not only efficiency but also the emotional satisfaction of the user.

[0553] "Traffic conditions" refers to information that indicates the flow and congestion of vehicles on the road.

[0554] "Weather conditions" refers to information that indicates the current or predicted weather conditions, and includes elements such as temperature, humidity, precipitation, and wind speed.

[0555] "Inventory status of goods" refers to information indicating the current quantity of a particular item in storage.

[0556] "Integrating data into a set" is the process of gathering and managing data obtained from different sources in one place.

[0557] "Analysis" is the process of thoroughly investigating and examining collected data to derive meaningful information.

[0558] "Setting delivery priority order" is the process of determining which of multiple delivery tasks should be executed first.

[0559] An "intelligent processing model" is a computational model that identifies patterns and relationships based on large amounts of data and autonomously seeks and presents the optimal solution.

[0560] "Creating a goods delivery route" is the process of designing a route for efficiently transporting goods from their origin to their destination.

[0561] In order to implement this invention, it is necessary to optimize the logistics system through a series of operations between the server, terminal, and user.

[0562] The server utilizes various databases and APIs to comprehensively manage traffic conditions, weather conditions, and inventory status of goods. Specifically, it uses a map information API to obtain traffic information and a weather forecast API to obtain weather data. Inventory information is appropriately retrieved from warehouse management systems and other sources. The server analyzes this data using Python's Pandas and NumPy to determine delivery priorities. TensorFlow and PyTorch are used in the generative artificial intelligence model to generate the optimal delivery route based on prompt messages.

[0563] The terminal provides an interface with the user and evaluates the user's emotional state through video and audio input. For this purpose, it uses general-purpose speech recognition software as its speech recognition engine, and a camera and the OpenCV library for facial expression analysis. The terminal detects the user's tone and facial expressions, collects emotional data, and sends it to the server.

[0564] Users can check their delivery schedule through the terminal interface and request adjustments as needed. For example, if delivery delays are expected due to heavy rain, users can check the situation via the terminal and express their dissatisfaction or requests verbally or through facial expressions.

[0565] For example, if a route requiring a detour due to heavy rain is presented, the prompt "Please propose the optimal delivery route considering current traffic congestion information and weather forecasts" is entered into the generating AI model. The server then calculates a new route and presents the result to the user via the terminal. This process enables efficient delivery while prioritizing user satisfaction.

[0566] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0567] Step 1:

[0568] The server collects data on traffic conditions, weather conditions, and inventory status of goods via APIs and database interfaces. Inputs are information obtained from various sensors and data provision services, and output is a well-organized dataset. For data processing, the server uses Python's Pandas library to integrate the data, impute missing values, and standardize the format.

[0569] Step 2:

[0570] The server analyzes the integrated dataset to determine delivery priorities. The input is the integrated dataset obtained in step 1, and the output is a prioritized delivery list. For data calculations, NumPy is used to score the impact of traffic congestion and weather, and then calculate the delivery priority accordingly.

[0571] Step 3:

[0572] The server uses a generative artificial intelligence model to generate the optimal goods delivery route. The input is the prioritized delivery list determined in step 2 and prompts for the generative AI model, and the output is the optimized delivery route. The server uses TensorFlow to generate the route from the prompts. Specifically, the input is a prompt such as "Please suggest a route that goes through major cities and avoids traffic congestion."

[0573] Step 4:

[0574] The terminal displays the generated delivery route on a map and prompts the user for confirmation. The input is the optimized delivery route obtained in step 3, and the output is visually displayed geographical information. This operation uses map display software to show the route in a way that is easy for the user to understand.

[0575] Step 5:

[0576] The device evaluates the user's emotional state through speech recognition and facial expression analysis. Input consists of the user's spoken words and facial expression data, while output is numerical data representing the user's emotional state. Google Speech-to-Text and OpenCV are used for emotion analysis, and the device monitors changes in the user's emotions in real time.

[0577] Step 6:

[0578] Users communicate their feedback and requests regarding the displayed delivery route to their device. This feedback is ultimately sent from the device to the server as sentiment data.

[0579] Step 7:

[0580] The server re-evaluates the delivery route based on sentiment data and readjusts the route as needed. The input is the user sentiment data obtained in step 6, and the output is the updated delivery route. For readjustment, the AI ​​model is reused to recalculate a flexible route that takes user emotions into account.

[0581] (Application Example 2)

[0582] 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."

[0583] Traditional material delivery systems, while optimizing using traffic, weather, and inventory information, have a problem in that they do not take into account the emotional state of the user and therefore do not sufficiently improve user satisfaction. Furthermore, because user dissatisfaction and discomfort are not reflected in the delivery plan, there is a risk of a decline in service quality. This is a concern as it could hinder the improvement of the customer experience.

[0584] 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.

[0585] In this invention, the server includes means for integrating traffic information, weather data, and material inventory information into a data storage facility; means for analyzing data acquired in real time and determining delivery priorities; means for generating an optimal material delivery route using an artificial intelligence structure; means for evaluating the user's emotional state using an emotion analysis engine; and means for readjusting the delivery route based on the user's emotional state. This enables flexible delivery planning that considers not only technical efficiency but also the user's emotional satisfaction, thereby improving the quality of service.

[0586] "Traffic information" refers to data on road conditions, traffic conditions, congestion, and traffic regulations.

[0587] "Weather data" refers to information that specifically describes meteorological conditions, such as precipitation, temperature, wind speed, and visibility.

[0588] "Information regarding the inventory of goods" refers to data on the quantity and condition of goods and items at distribution centers and stores.

[0589] A "data storage facility" refers to databases and servers used to integrate and store information.

[0590] "Artificial intelligence structure" refers to a system based on algorithms and models designed for the purpose of solving a specific problem.

[0591] The term "emotion analysis engine" refers to a technological foundation for evaluating a user's emotional state by performing speech recognition, facial expression analysis, and contextual analysis.

[0592] "User emotional state" refers to the mental reactions and feedback exhibited by individual users, and represents a state in which satisfaction and dissatisfaction levels can be quantified.

[0593] "Means of readjusting delivery routes" refers to processes or systems for dynamically changing existing route plans based on collected data.

[0594] To implement this invention, it is necessary to construct a system in which a server and a terminal work in cooperation. First, the server integrates traffic information, weather data, and material inventory information into a data storage facility and analyzes this data in real time. Based on the analysis results, the server determines delivery priorities and then generates the optimal material delivery route using an artificial intelligence structure that it generates. This generated route information is provided to the terminal through a display device.

[0595] The device is equipped with an emotion analysis engine that evaluates the user's emotional state through speech recognition, facial expression analysis, and contextual analysis. This allows it to detect how the user is emotionally responding to the delivery plan and send that data to the server. Based on the received data on the user's emotional state, the server readjusts the delivery route as needed, providing a flexible delivery plan.

[0596] The hardware used includes server equipment and smartphones or tablet devices used by users, while the software includes EmotionAnalyzer for analyzing emotions and DeliveryRouteOptimizer for optimizing delivery routes.

[0597] For example, if a delivery may be delayed due to heavy rain, and the terminal's emotion engine detects user dissatisfaction, the server will respond quickly and suggest an alternative route to mitigate user dissatisfaction.

[0598] An example of a prompt for a generative AI model is: "Analyze the user's emotions and suggest adjustments to the delivery route. If the user is unhappy due to heavy rain, what is the best route?"

[0599] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0600] Step 1:

[0601] The server collects traffic information, weather data, and material inventory information from external data sources. This information is integrated into a data storage facility. Inputs are various data obtained from external APIs and stored in a database, enabling real-time access. Output is an integrated dataset.

[0602] Step 2:

[0603] The server analyzes and processes the integrated data to determine delivery priorities. The input is an integrated dataset, and the output is delivery priority information obtained as a result of the analysis. During the analysis process, specific algorithms are used to predict traffic congestion and sudden weather changes.

[0604] Step 3:

[0605] The server uses a generated artificial intelligence structure to calculate the optimal supply delivery route. This process optimizes routes based on priority. Inputs are delivery priority information and current traffic and weather data, and output is the optimized delivery route. The generated AI model assists in effective route calculation.

[0606] Step 4:

[0607] The server transmits calculated delivery route information to the terminal. The terminal is equipped with a display device that visually presents the delivery plan to the user. Specifically, the display includes the route and estimated travel time clearly shown on a map. The input is the delivery route data sent from the server, and the output is the information displayed on the terminal's user interface.

[0608] Step 5:

[0609] The device uses an emotion analysis engine to evaluate the user's emotional state from their voice and facial expressions. Input is the user's voice tone and facial expression information, while output is the user's emotional state data obtained through analysis. It integrates cutting-edge voice analysis and image processing technologies to measure user responses in real time.

[0610] Step 6:

[0611] The user's emotional state data is sent from the terminal to the server. The server readjusts the delivery route based on this data. The inputs here are the user's emotional state data and the current delivery route, and the output is the readjusted delivery route. If emotional dissatisfaction is detected, the server will suggest alternative routes or adjustment plans.

[0612] Step 7:

[0613] Users review the newly adjusted delivery plan through their terminal. This improves the service experience by allowing users to receive flexible responses tailored to their satisfaction and circumstances. The input is the re-adjusted delivery route from the server, and the output is the presentation of the new delivery plan to the user.

[0614] Through these steps, efficient material delivery is achieved while taking user emotions into consideration.

[0615] 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.

[0616] 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.

[0617] 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.

[0618] [Fourth Embodiment]

[0619] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0620] 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.

[0621] 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).

[0622] 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.

[0623] 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.

[0624] 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).

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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".

[0632] This invention is a system that collects and integrates traffic information, weather data, and material inventory information in real time to enable effective material distribution. This system has a three-tiered structure consisting of a server, terminals, and users, each playing a different role.

[0633] The server forms the core of this system, collecting and analyzing data. It acquires real-time data from traffic information services, weather forecasting services, and warehouse management systems, and integrates it. Using this integrated data, the system can quickly set delivery priorities for goods. Furthermore, the server utilizes a generative AI model to automatically generate optimal delivery routes that take into account traffic congestion and weather conditions.

[0634] The terminal functions as a user interface, providing a means for the user to input necessary information. Through the terminal, the user can input the type, quantity, and priority of necessary supplies. The terminal transmits the input information to the server and visually displays the generated delivery plan.

[0635] Users can view the delivery plan provided by the server through their terminal and provide feedback as needed. The server uses this feedback to dynamically adjust delivery routes and priorities, increasing the flexibility of the plan.

[0636] As a concrete example, consider a scenario involving a large-scale natural disaster. The server acquires traffic and weather data from the affected area and generates the optimal delivery route based on the priority of supplies. In doing so, it takes into account road closures and severe weather conditions, while ensuring priority delivery to evacuation centers that are short on supplies. The terminal presents this delivery plan to the user, and the server flexibly modifies the plan based on additional requests and modification instructions from the user.

[0637] This system is expected to enable the rapid and effective delivery of supplies during disasters.

[0638] The following describes the processing flow.

[0639] Step 1:

[0640] The server retrieves traffic information, weather data, and material inventory information in real time from multiple external data sources. This includes obtaining information from traffic APIs and weather APIs.

[0641] Step 2:

[0642] The server centrally integrates the acquired data, formats it, and organizes it into a consistent database. This eliminates duplicate data and standardizes the format.

[0643] Step 3:

[0644] Users input the type, quantity, and priority of necessary supplies via a terminal. This information is sent to the server as basic data for the supply delivery plan.

[0645] Step 4:

[0646] The server analyzes real-time traffic and weather data to determine delivery priorities. This includes analysis that takes into account congestion and weather conditions.

[0647] Step 5:

[0648] The server uses a generative artificial intelligence model to generate the optimal delivery route. This AI model calculates the route that maximizes delivery efficiency based on the acquired data.

[0649] Step 6:

[0650] The terminal receives the delivery route generated from the server and displays it in the user interface. Users can view visualized maps and detailed lists.

[0651] Step 7:

[0652] Users can check delivery routes via their devices and send feedback or change requests to the server as needed. This feedback can include specific delivery needs and changes.

[0653] Step 8:

[0654] The server receives user feedback, re-evaluates delivery routes, and dynamically adjusts them as needed. This enables flexible and adaptive delivery planning.

[0655] Step 9:

[0656] The terminal then provides the user with the updated delivery plan again and supports final confirmation and approval. This ensures reliable delivery of goods.

[0657] (Example 1)

[0658] 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".

[0659] There is a need to respond quickly to changes in traffic conditions, weather conditions, and fluctuations in material inventory, and to deliver goods efficiently. However, conventional systems have difficulty integrating this diverse information in real time, making it difficult to flexibly generate and adjust optimal delivery routes. Furthermore, there is a lack of mechanisms to appropriately revise plans based on dynamic feedback from users, resulting in problems with effective material delivery.

[0660] 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.

[0661] In this invention, the server includes means for integrating traffic information, weather data, and inventory data into a data set; means for analyzing information acquired in real time and determining delivery priorities; and means for generating an optimal transportation route based on prompt messages using a generative artificial intelligence model. This makes it possible to formulate an optimal delivery plan based on information that changes in real time and to flexibly adjust the plan by utilizing user feedback.

[0662] "Traffic information" refers to information that shows the flow of traffic, congestion levels, and passable routes.

[0663] "Weather data" refers to information related to weather, such as weather conditions, forecasts, temperature, and precipitation.

[0664] "Inventory data" refers to management information such as the quantity and location of goods.

[0665] A "data set" is a collection of information that integrates multiple data points obtained from different sources.

[0666] "Real-time" refers to a state where the time between information acquisition and processing is instantaneous.

[0667] A "generative artificial intelligence model" is a computer program that implements algorithms for making predictions and optimizations based on input data.

[0668] A "prompt" is a text that describes instructions or questions given to a generative artificial intelligence model in order to produce an appropriate output.

[0669] A "transportation route" refers to a specific path or route planned for the efficient movement of goods.

[0670] This invention is a system that integrates traffic information, weather data, and inventory data in real time to achieve optimal material delivery. This system mainly consists of three elements: a server, terminals, and users.

[0671] The server functions as the central hub of this system, collecting information from diverse data sources. Specifically, it utilizes traffic information services to understand traffic conditions, weather forecasting services to obtain weather predictions, and a warehouse management system to manage inventory levels. This data is integrated into the server and analyzed immediately. A generative AI model is used for analysis, and appropriate transportation routes are calculated based on the input of prompts. An example of a prompt is, "Consider the traffic and weather data for the specified area, and create the optimal supply delivery route to facilities experiencing shortages."

[0672] The terminal serves as the user interface. Through the terminal, users can input necessary material information and verify the generated transportation route. Furthermore, the terminal also acts as a medium for sending user feedback to the server.

[0673] Users can participate in the system in real time by using these terminals to efficiently manage supplies, check delivery plans, and provide feedback as needed. This participation enables the system to operate quickly and flexibly.

[0674] A concrete example is the emergency delivery of supplies during a disaster. Users input information about the supplies they need immediately into their terminals, and then review and adjust the delivery routes generated based on the latest traffic and weather data provided by the server. This enables the most effective delivery plan and allows for the immediate provision of necessary assistance.

[0675] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0676] Step 1:

[0677] The server retrieves data from traffic information services, weather forecasting services, and warehouse management systems. The input is raw data obtained through API requests from these services. This allows for the collection of real-time data on traffic conditions, weather conditions, and inventory levels. This data is then integrated within a database.

[0678] Step 2:

[0679] The server analyzes the acquired data to determine the priority of supply delivery. The input is the data integrated in step 1. This data is analyzed, and priorities are assigned based on specific conditions (e.g., insufficient stock, urgency). The output is a dataset with the assigned priorities.

[0680] Step 3:

[0681] The server inputs a prompt message into the generating AI model to generate the optimal transportation route. The input consists of the prioritized data obtained in step 2 and the prompt message: "Consider the traffic and weather data for the specified area and create the optimal supply delivery route to the facility experiencing shortages." Using the generating AI model, the optimal route that takes traffic congestion and weather into account is output.

[0682] Step 4:

[0683] The terminal visually displays the generated transport route to the user. The input is the transport route generated by the server in step 3. Visualizing this on a map makes it easy for the user to understand the transport plan. The output is the detailed transport route presented to the user.

[0684] Step 5:

[0685] Users provide feedback through their devices, and the delivery route is adjusted as needed. The input consists of the user's opinions and requests regarding the delivery plan presented in step 4. The output is sent to the server as a revised request and reflected in the next plan. This improves the flexibility of the delivery plan.

[0686] (Application Example 1)

[0687] 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".

[0688] To achieve rapid and efficient delivery of goods at logistics facilities, it is necessary to quickly generate and dynamically adjust optimal delivery routes that take into account traffic information and weather conditions. However, current technology does not adequately modify or optimize delivery plans in real time, leading to problems such as reduced delivery efficiency and delays. Furthermore, it is difficult to immediately incorporate feedback from delivery personnel, and the inability to flexibly adjust routes is a challenge.

[0689] 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.

[0690] In this invention, the server includes means for integrating traffic information, weather data, and inventory information of goods into an information recording device; means for analyzing the information acquired in real time and determining the delivery order; and means for generating the optimal material delivery route using a generative artificial intelligence model. This enables logistics facility managers and transportation personnel to obtain the optimal delivery route in real time and improve delivery efficiency. Furthermore, a feedback function using a communication terminal allows for dynamic modification of the delivery plan and maintenance of the optimal route.

[0691] "Traffic information" refers to real-time data on transportation conditions, such as road congestion and traffic restrictions.

[0692] "Weather data" refers to real-time numerical information about weather conditions, such as weather, temperature, precipitation, and wind speed.

[0693] "Goods inventory information" refers to data regarding the quantity and condition of goods in warehouses and logistics facilities.

[0694] An "information recording device" is a computer system or database used to integrate and store various types of data.

[0695] "Analysis" is the process of processing acquired information to derive meaningful results from data.

[0696] "Delivery order" refers to the criteria used to determine the priority and order in which goods are delivered.

[0697] A "generative artificial intelligence model" is an algorithm or program that uses machine learning to generate the optimal delivery route.

[0698] A "delivery route" is a plan that indicates the optimal route for delivering goods.

[0699] A "display device" is a device, such as a computer or smartphone, that visually displays information.

[0700] A "communication terminal" is a portable information processing device capable of data communication, such as a smartphone or tablet.

[0701] "Real-time feedback" is a communication function that instantly receives and reflects information from the sender.

[0702] "Dynamic reconstruction" refers to the process of updating and modifying existing plans and structures in real time based on new information from external sources.

[0703] The system that realizes this invention mainly consists of three components: a server, a terminal, and a user.

[0704] The server aggregates traffic information, weather data, and inventory information in real time and integrates it into an information recording device. External services such as the Google Maps API and weather data API are used to collect information. The collected data is analyzed using a Python script, and a generative artificial intelligence model generates the optimal delivery route. This generation process includes optimization calculations that take traffic and weather conditions into account, and the generated delivery route is provided to the terminal in real time.

[0705] The terminals are smartphones and tablets used by logistics facility managers and transportation personnel. The generated delivery plans are displayed graphically on the terminals, allowing each user to plan their actions based on them. Furthermore, they feature real-time feedback, enabling dynamic modifications to the delivery plan by providing delivery status and new information to the server.

[0706] Users can check delivery routes and priorities through their devices and provide feedback as needed. This feedback is sent to the server, and the delivery plan is automatically readjusted. This feature allows for flexible and immediate responses, even in cases such as sudden changes in weather or unexpected traffic congestion.

[0707] As a concrete example, consider a logistics facility efficiently handling the peak delivery period during the Christmas season. This system makes it possible to optimize routes while minimizing delays, even while taking into account worsening weather and traffic conditions.

[0708] An example of a prompt to the generating AI model would be: "Based on current traffic data and weather conditions, generate the optimal route to the following delivery destinations. Please pay particular attention to reducing estimated arrival times in bad weather." In response to this prompt, the AI ​​will propose the optimal route.

[0709] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0710] Step 1:

[0711] The server collects traffic information, weather data, and inventory information in real time using the Google Maps API and weather data API. This information is integrated into an information recording device. As input, data from each API is retrieved and stored in a database. As output, an integrated dataset is prepared.

[0712] Step 2:

[0713] The server analyzes the integrated dataset using a Python script to determine the delivery order. The input is the integrated dataset from step 1, and the priority and necessity of each data point are evaluated. The output is a list of prioritized delivery tasks.

[0714] Step 3:

[0715] The server uses a generated artificial intelligence model to create the optimal delivery route using prompts. The inputs here are the prompts and the list of delivery tasks obtained in step 2. The generated AI model calculates the optimal route, and the output is a specific delivery route.

[0716] Step 4:

[0717] The server sends the generated delivery route to the terminal, which then displays it visually on the display device. The input is the delivery route generated in step 3, and the output is the plan displayed on the screen of a terminal used by logistics facility managers or transportation personnel.

[0718] Step 5:

[0719] Users can view delivery plans via their devices and provide real-time feedback as needed. Input is the displayed delivery route, and users provide feedback. Output is the user's feedback information sent to the server.

[0720] Step 6:

[0721] The server readjusts the delivery plan based on user feedback. The input here is the feedback information obtained in step 5. Through data processing, the AI ​​recalculates, and the output is an updated delivery route. This updated information is sent back to the terminal, ensuring that the optimal route is always maintained.

[0722] 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.

[0723] This invention combines a system that utilizes traffic information, weather data, and inventory information to achieve optimal material delivery with an emotion engine. The emotion engine recognizes the user's emotional state and uses this to adjust the delivery plan.

[0724] The server collects and integrates traffic information, weather data, and material inventory information as before. It performs real-time analysis of this data to determine delivery priorities. Furthermore, it can use generative artificial intelligence models to generate optimal material delivery routes based on the existing information.

[0725] The terminal is responsible for the user interface and is equipped with an emotion engine. This emotion engine evaluates the user's emotions through speech recognition, facial expression analysis, and contextual analysis. This makes it possible to quantify how the user is reacting to the delivery plan and the degree of dissatisfaction or satisfaction.

[0726] The user enters delivery plan details using a terminal and confirms the visually displayed delivery route. Simultaneously, an emotion engine analyzes the user's tone of voice and facial expressions and sends this emotion data to the server. Based on this emotion data, the server readjusts the delivery route as needed. This enables flexible delivery planning that considers not only efficiency but also the user's emotional satisfaction.

[0727] As a concrete example, suppose a user receives a delivery route that requires a detour due to heavy rain. The terminal detects the user's dissatisfaction with this using an emotion engine and transmits it to the server. The server considers this emotion data, reconsiders the route, changes priorities, and provides a plan that satisfies the user. In this way, this system combines technical optimization with improved user experience.

[0728] The following describes the processing flow.

[0729] Step 1:

[0730] The server acquires and integrates real-time traffic information, weather data, and inventory information from external sources. This creates a database based on the latest road and weather conditions.

[0731] Step 2:

[0732] The server analyzes the integrated data to determine priorities for efficient supply delivery. This analysis includes decisions that take into account weather conditions and the presence or absence of traffic disruptions.

[0733] Step 3:

[0734] The terminal provides an input interface that accepts the type, quantity, and priority of supplies the user needs. The user uses this interface to input the information.

[0735] Step 4:

[0736] The server receives user input data and uses a generative artificial intelligence model to generate the optimal delivery route. This route takes into account the acquired data and the importance of the user.

[0737] Step 5:

[0738] The terminal displays the generated delivery route to the user, presenting it in a visualized map format for easy confirmation.

[0739] Step 6:

[0740] The device monitors the user's emotional state through an emotion engine. It analyzes the user's emotions towards the delivery plan through voice and facial recognition.

[0741] Step 7:

[0742] The emotional data analyzed by the emotion engine is sent to the server in real time. The server uses this emotional data to re-evaluate delivery routes and priorities.

[0743] Step 8:

[0744] The server dynamically adjusts the delivery route based on the user's emotional state, thereby presenting a delivery plan that is more satisfying to the user.

[0745] Step 9:

[0746] The terminal notifies the user again of the adjusted delivery route and provides an interface for final confirmation and approval. This allows for user feedback before implementation, enabling further adjustments.

[0747] (Example 2)

[0748] 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".

[0749] While conventional material delivery systems could determine efficient delivery routes based on traffic and weather data, they could not implement flexible delivery plans that considered user emotional satisfaction. This made it difficult to respond quickly to user dissatisfaction and requests, resulting in a challenge in providing an optimal user experience.

[0750] 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.

[0751] In this invention, the server includes means for integrating traffic conditions, weather conditions, and inventory status of goods into a data set; means for analyzing the data acquired at a given time and setting delivery priority orders; and means for creating an optimal goods delivery route using an intelligent processing model. This enables flexible delivery planning that takes into account not only efficiency but also the emotional satisfaction of the user.

[0752] "Traffic conditions" refers to information that indicates the flow and congestion of vehicles on the road.

[0753] "Weather conditions" refers to information that indicates the current or predicted weather conditions, and includes elements such as temperature, humidity, precipitation, and wind speed.

[0754] "Inventory status of goods" refers to information indicating the current quantity of a particular item in storage.

[0755] "Integrating data into a set" is the process of gathering and managing data obtained from different sources in one place.

[0756] "Analysis" is the process of thoroughly investigating and examining collected data to derive meaningful information.

[0757] "Setting delivery priority order" is the process of determining which of multiple delivery tasks should be executed first.

[0758] An "intelligent processing model" is a computational model that identifies patterns and relationships based on large amounts of data and autonomously seeks and presents the optimal solution.

[0759] "Creating a goods delivery route" is the process of designing a route for efficiently transporting goods from their origin to their destination.

[0760] In order to implement this invention, it is necessary to optimize the logistics system through a series of operations between the server, terminal, and user.

[0761] The server utilizes various databases and APIs to comprehensively manage traffic conditions, weather conditions, and inventory status of goods. Specifically, it uses a map information API to obtain traffic information and a weather forecast API to obtain weather data. Inventory information is appropriately retrieved from warehouse management systems and other sources. The server analyzes this data using Python's Pandas and NumPy to determine delivery priorities. TensorFlow and PyTorch are used in the generative artificial intelligence model to generate the optimal delivery route based on prompt messages.

[0762] The terminal provides an interface with the user and evaluates the user's emotional state through video and audio input. For this purpose, it uses general-purpose speech recognition software as its speech recognition engine, and a camera and the OpenCV library for facial expression analysis. The terminal detects the user's tone and facial expressions, collects emotional data, and sends it to the server.

[0763] Users can check their delivery schedule through the terminal interface and request adjustments as needed. For example, if delivery delays are expected due to heavy rain, users can check the situation via the terminal and express their dissatisfaction or requests verbally or through facial expressions.

[0764] For example, if a route requiring a detour due to heavy rain is presented, the prompt "Please propose the optimal delivery route considering current traffic congestion information and weather forecasts" is entered into the generating AI model. The server then calculates a new route and presents the result to the user via the terminal. This process enables efficient delivery while prioritizing user satisfaction.

[0765] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0766] Step 1:

[0767] The server collects data on traffic conditions, weather conditions, and inventory status of goods via APIs and database interfaces. Inputs are information obtained from various sensors and data provision services, and output is a well-organized dataset. For data processing, the server uses Python's Pandas library to integrate the data, impute missing values, and standardize the format.

[0768] Step 2:

[0769] The server analyzes the integrated dataset to determine delivery priorities. The input is the integrated dataset obtained in step 1, and the output is a prioritized delivery list. For data calculations, NumPy is used to score the impact of traffic congestion and weather, and then calculate the delivery priority accordingly.

[0770] Step 3:

[0771] The server uses a generative artificial intelligence model to generate the optimal goods delivery route. The input is the prioritized delivery list determined in step 2 and prompts for the generative AI model, and the output is the optimized delivery route. The server uses TensorFlow to generate the route from the prompts. Specifically, the input is a prompt such as "Please suggest a route that goes through major cities and avoids traffic congestion."

[0772] Step 4:

[0773] The terminal displays the generated delivery route on a map and prompts the user for confirmation. The input is the optimized delivery route obtained in step 3, and the output is visually displayed geographical information. This operation uses map display software to show the route in a way that is easy for the user to understand.

[0774] Step 5:

[0775] The device evaluates the user's emotional state through speech recognition and facial expression analysis. Input consists of the user's spoken words and facial expression data, while output is numerical data representing the user's emotional state. Google Speech-to-Text and OpenCV are used for emotion analysis, and the device monitors changes in the user's emotions in real time.

[0776] Step 6:

[0777] Users communicate their feedback and requests regarding the displayed delivery route to their device. This feedback is ultimately sent from the device to the server as sentiment data.

[0778] Step 7:

[0779] The server re-evaluates the delivery route based on sentiment data and readjusts the route as needed. The input is the user sentiment data obtained in step 6, and the output is the updated delivery route. For readjustment, the AI ​​model is reused to recalculate a flexible route that takes user emotions into account.

[0780] (Application Example 2)

[0781] 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".

[0782] Traditional material delivery systems, while optimizing using traffic, weather, and inventory information, have a problem in that they do not take into account the emotional state of the user and therefore do not sufficiently improve user satisfaction. Furthermore, because user dissatisfaction and discomfort are not reflected in the delivery plan, there is a risk of a decline in service quality. This is a concern as it could hinder the improvement of the customer experience.

[0783] 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.

[0784] In this invention, the server includes means for integrating traffic information, weather data, and material inventory information into a data storage facility; means for analyzing data acquired in real time and determining delivery priorities; means for generating an optimal material delivery route using an artificial intelligence structure; means for evaluating the user's emotional state using an emotion analysis engine; and means for readjusting the delivery route based on the user's emotional state. This enables flexible delivery planning that considers not only technical efficiency but also the user's emotional satisfaction, thereby improving the quality of service.

[0785] "Traffic information" refers to data on road conditions, traffic conditions, congestion, and traffic regulations.

[0786] "Weather data" refers to information that specifically describes meteorological conditions, such as precipitation, temperature, wind speed, and visibility.

[0787] "Information regarding the inventory of goods" refers to data on the quantity and condition of goods and items at distribution centers and stores.

[0788] A "data storage facility" refers to databases and servers used to integrate and store information.

[0789] "Artificial intelligence structure" refers to a system based on algorithms and models designed for the purpose of solving a specific problem.

[0790] The term "emotion analysis engine" refers to a technological foundation for evaluating a user's emotional state by performing speech recognition, facial expression analysis, and contextual analysis.

[0791] "User emotional state" refers to the mental reactions and feedback exhibited by individual users, and represents a state in which satisfaction and dissatisfaction levels can be quantified.

[0792] "Means of readjusting delivery routes" refers to processes or systems for dynamically changing existing route plans based on collected data.

[0793] To implement this invention, it is necessary to construct a system in which a server and a terminal work in cooperation. First, the server integrates traffic information, weather data, and material inventory information into a data storage facility and analyzes this data in real time. Based on the analysis results, the server determines delivery priorities and then generates the optimal material delivery route using an artificial intelligence structure that it generates. This generated route information is provided to the terminal through a display device.

[0794] The device is equipped with an emotion analysis engine that evaluates the user's emotional state through speech recognition, facial expression analysis, and contextual analysis. This allows it to detect how the user is emotionally responding to the delivery plan and send that data to the server. Based on the received data on the user's emotional state, the server readjusts the delivery route as needed, providing a flexible delivery plan.

[0795] The hardware used includes server equipment and smartphones or tablet devices used by users, while the software includes EmotionAnalyzer for analyzing emotions and DeliveryRouteOptimizer for optimizing delivery routes.

[0796] For example, if a delivery may be delayed due to heavy rain, and the terminal's emotion engine detects user dissatisfaction, the server will respond quickly and suggest an alternative route to mitigate user dissatisfaction.

[0797] An example of a prompt for a generative AI model is: "Analyze the user's emotions and suggest adjustments to the delivery route. If the user is unhappy due to heavy rain, what is the best route?"

[0798] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0799] Step 1:

[0800] The server collects traffic information, weather data, and material inventory information from external data sources. This information is integrated into a data storage facility. Inputs are various data obtained from external APIs and stored in a database, enabling real-time access. Output is an integrated dataset.

[0801] Step 2:

[0802] The server analyzes and processes the integrated data to determine delivery priorities. The input is an integrated dataset, and the output is delivery priority information obtained as a result of the analysis. During the analysis process, specific algorithms are used to predict traffic congestion and sudden weather changes.

[0803] Step 3:

[0804] The server uses a generated artificial intelligence structure to calculate the optimal supply delivery route. This process optimizes routes based on priority. Inputs are delivery priority information and current traffic and weather data, and output is the optimized delivery route. The generated AI model assists in effective route calculation.

[0805] Step 4:

[0806] The server transmits calculated delivery route information to the terminal. The terminal is equipped with a display device that visually presents the delivery plan to the user. Specifically, the display includes the route and estimated travel time clearly shown on a map. The input is the delivery route data sent from the server, and the output is the information displayed on the terminal's user interface.

[0807] Step 5:

[0808] The device uses an emotion analysis engine to evaluate the user's emotional state from their voice and facial expressions. Input is the user's voice tone and facial expression information, while output is the user's emotional state data obtained through analysis. It integrates cutting-edge voice analysis and image processing technologies to measure user responses in real time.

[0809] Step 6:

[0810] The user's emotional state data is sent from the terminal to the server. The server readjusts the delivery route based on this data. The inputs here are the user's emotional state data and the current delivery route, and the output is the readjusted delivery route. If emotional dissatisfaction is detected, the server will suggest alternative routes or adjustment plans.

[0811] Step 7:

[0812] Users review the newly adjusted delivery plan through their terminal. This improves the service experience by allowing users to receive flexible responses tailored to their satisfaction and circumstances. The input is the re-adjusted delivery route from the server, and the output is the presentation of the new delivery plan to the user.

[0813] Through these steps, efficient material delivery is achieved while taking user emotions into consideration.

[0814] 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.

[0815] 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.

[0816] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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."

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0835] The following is further disclosed regarding the embodiments described above.

[0836] (Claim 1)

[0837] A means of integrating traffic information, weather data, and material inventory information into a database,

[0838] A means of analyzing data acquired in real time to determine delivery priorities,

[0839] A means of generating the optimal material delivery route using a generative artificial intelligence model,

[0840] Means for providing the generated delivery route to a display device,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, further comprising means for automatically adjusting high-priority material delivery plans.

[0844] (Claim 3)

[0845] The system according to claim 1, further comprising means for receiving user feedback and dynamically readjusting delivery routes.

[0846] "Example 1"

[0847] (Claim 1)

[0848] A means of integrating traffic information, weather data, and inventory data into a data set,

[0849] A means of analyzing information acquired in real time to determine delivery priority,

[0850] A means for generating the optimal transport route based on a prompt sentence using a generative artificial intelligence model,

[0851] Means for providing the generated transport route to a display device,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, further comprising means for automatically adjusting high-priority material transport plans.

[0855] (Claim 3)

[0856] The system according to claim 1, further comprising means for receiving feedback from users and dynamically readjusting the transport route.

[0857] "Application Example 1"

[0858] (Claim 1)

[0859] A means for integrating traffic information, weather data, and inventory information of goods into an information recording device,

[0860] A means of analyzing information acquired in real time and determining the delivery order,

[0861] A means for generating the optimal material delivery route using a generative artificial intelligence model,

[0862] Means for providing the generated delivery route to a display device,

[0863] A means of acquiring information in real time and adjusting routes using a communication terminal used by the manager or transportation personnel of a logistics facility,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, which includes means for automatically modifying high-priority material delivery plans and enables real-time feedback via a communication terminal.

[0867] (Claim 3)

[0868] The system according to claim 1, comprising means for receiving information from delivery personnel and dynamically reconstructing the delivery route.

[0869] "Example 2 of combining an emotion engine"

[0870] (Claim 1)

[0871] A means of integrating traffic conditions, weather conditions, and inventory status of goods into a data set,

[0872] A means of analyzing data acquired at a given time and setting delivery priorities,

[0873] A means of creating the optimal goods delivery route using an intelligent processing model,

[0874] A means for transmitting the created delivery route to an output device,

[0875] A means for evaluating the user's emotional state using an emotion analysis engine and transmitting that information to a data processing device,

[0876] A means of flexibly adjusting delivery routes based on user emotional data,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, further comprising means for automatically improving high-priority goods delivery plans.

[0880] (Claim 3)

[0881] The system according to claim 1, further comprising means for receiving emotional data from users and dynamically resetting the delivery route.

[0882] "Application example 2 when combining with an emotional engine"

[0883] (Claim 1)

[0884] A means of integrating traffic information, weather data, and material inventory information into a data storage facility,

[0885] A means of analyzing data acquired in real time to determine delivery priorities,

[0886] A means for generating the optimal material delivery route using the generated artificial intelligence structure,

[0887] Means for providing the generated delivery route to a display device,

[0888] A means of evaluating a user's emotional state using an emotion analysis engine,

[0889] A means of readjusting delivery routes based on the user's emotional state,

[0890] A system that includes this.

[0891] (Claim 2)

[0892] The system according to claim 1, further comprising means for automatically adjusting the schedule for the delivery of high-priority supplies.

[0893] (Claim 3)

[0894] The system according to claim 1, further comprising means for quantifying user responses and dynamically readjusting delivery routes. [Explanation of Symbols]

[0895] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of integrating traffic information, weather data, and material inventory information into a database, A means of analyzing data acquired in real time to determine delivery priorities, A means of generating the optimal material delivery route using a generative artificial intelligence model, Means for providing the generated delivery route to a display device, A system that includes this.

2. The system according to claim 1, further comprising means for automatically adjusting high-priority material delivery plans.

3. The system according to claim 1, further comprising means for receiving user feedback and dynamically readjusting delivery routes.

Images