System

Abstract

A system is provided.SOLUTION: The system includes a data collection means for collecting road condition and weather information in real time, a data analysis means for analyzing the congestion degree of the road, the weather change, and the driver's fatigue degree based on the collected data, and a route optimization means for creating an optimum physical distribution route based on the analysis result and preparing an operation plan in consideration of the load of the driver.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In the logistics industry, the burden on drivers due to long-distance and long-time driving is a major problem. Furthermore, it is often difficult to select an efficient route, which increases transportation costs. The purpose of this invention is to solve these problems and provide an efficient logistics system with low burden. [Means for solving the problem]

[0005] The present invention provides a system that includes a data collection means for collecting road condition and weather information in real time, a data analysis means for analyzing road congestion, weather fluctuations, and driver fatigue based on the collected data, and a route optimization means for generating an optimal logistics route based on the analysis results and creating an operation plan that takes the driver's workload into consideration. The system also includes a display means for displaying the optimal route and operation plan to the driver and notifying them of updates to traffic information and weather forecasts in real time. The system also includes a feedback collection means for collecting feedback from the driver and transmitting it to a server to improve the route, thereby achieving efficient logistics and reducing the driver's workload.

[0006] "Data Collection Measures" means hardware, software, or a combination of both for obtaining real-time road condition and weather information from external sources.

[0007] "Data analysis means" refers to algorithms and models that use collected data to analyze road congestion, weather fluctuations, and driver fatigue.

[0008] The "route optimization means" is a system for generating optimal logistics routes and operation plans based on the analysis results of the data analysis means.

[0009] The "display means" is a terminal that presents the optimum route and operation plan generated above to the driver, and also notifies them of real-time traffic information and weather forecast updates.

[0010] A "feedback collection means" is a system that collects feedback from drivers and sends that information to a server to help improve routes. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2]1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0012] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0013] First, the terms used in the following description will be explained.

[0014] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0015] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0016] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0017] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0019] [First embodiment]

[0020] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0021] 1, a 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.

[0022] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0024] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0025] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0027] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0029] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0031] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0032] This invention is a new logistics system that utilizes generative AI. This system collects road and weather information in real time and can quickly propose optimal logistics routes. It also provides plans to minimize driver workload, aiming to reduce transportation costs and improve driver working conditions.

[0033] Server Operation

[0034] Data collection

[0035] The server obtains real-time road and weather information from external APIs. This allows it to collect current traffic volume, accident information, weather conditions, and more. It also obtains cargo information, driver work status, and vehicle status from the logistics management system. For example, the server obtains road information from the Google (registered trademark) Maps API and weather information from the OpenWeatherMap API, while simultaneously understanding the driver's work status from the traffic management system.

[0036] Data analysis

[0037] The collected information is analyzed on a server. An AI algorithm is used to analyze the collected data and evaluate road congestion, weather fluctuations, and driver fatigue. Delivery priorities are also determined taking into account product characteristics (weight, volume, priority, etc.). For example, an AI model analyzes road congestion and weather fluctuations to generate a delivery plan that prioritizes the delivery of high-priority products.

[0038] Route Optimization

[0039] Based on the analysis results, the server generates an optimal logistics route. The generated route takes into account the driver's workload, avoids long periods of continuous driving, and includes regular rest stops. For example, the server may suggest detour routes to avoid traffic jams and create a plan for efficient delivery by using expressways.

[0040] Device behavior

[0041] Displaying instructions

[0042] The device displays the optimized route and operation plan sent from the server to the driver. It also notifies the driver of traffic information and weather forecast updates in real time. For example, the device displays specific instructions such as "The next right turn is 2 kilometers away. At your current speed, you can reach it within 10 minutes."

[0043] Collecting feedback

[0044] The device collects feedback from the driver. When the driver inputs road conditions or changes in weather into the device, that information is immediately sent to the server, and the route is improved. For example, if the driver inputs "delays due to road construction" into the device, that information is sent to the server and reanalyzed.

[0045] User Actions

[0046] Check the route

[0047] The user (driver) checks the route displayed on the device and starts driving according to the instructions. For example, the user follows the instructions on the device to turn right and proceed to the next destination.

[0048] Providing Feedback

[0049] Users input changes in traffic conditions and weather into their devices. If there is new information, it is immediately entered into the device and sent to the server, which then revises the route. For example, "the user can input that the road is congested into the device, and that data is sent to the server, which then generates a new optimal route."

[0050] This system will reduce the burden on drivers while improving logistics efficiency and reducing costs.

[0051] The processing flow will be explained below.

[0052] Step 1:

[0053] The server collects road conditions and weather information in real time.

[0054] The server periodically obtains current traffic and weather information using external APIs. Specifically, the server uses the Google Maps API to collect road congestion and accident information, and the OpenWeatherMap API to obtain current weather information. The server also obtains cargo information and driver work status from the logistics management system (WMS) and the transportation management system (TMS).

[0055] Step 2:

[0056] The server performs analysis based on the collected data.

[0057] The data collected on the server is analyzed using an AI model. The analysis evaluates road congestion, weather fluctuations, and driver fatigue. Product characteristics (weight, volume, priority) are also taken into account to determine delivery priorities. For example, the server inputs the collected data into the AI ​​to determine whether a detour route is necessary if a particular road is congested.

[0058] Step 3:

[0059] The server generates optimal routes and operation plans.

[0060] Based on the results of the data analysis, the server generates an optimal logistics route that takes into account the driver's workload. The generated route includes rest stops for the driver and a schedule to prevent continuous driving of less than five hours. For example, the server suggests using highways or taking shorter routes to avoid traffic jams.

[0061] Step 4:

[0062] The terminal displays the optimal route and operation plan received from the server to the driver.

[0063] The device receives data from the server and displays the optimal route and trip plan to the driver. It also provides real-time updates on traffic and weather forecasts. For example, the device might display specific instructions such as, "The next right turn is 2 kilometers away. At your current speed, you can reach it within 10 minutes."

[0064] Step 5:

[0065] The user checks the displayed route and starts driving.

[0066] The user (driver) checks the route displayed on the terminal and starts driving according to the instructions, for example, driving the car towards the destination according to the clearly indicated route.

[0067] Step 6:

[0068] The device collects feedback from the driver.

[0069] The device accepts feedback from the driver. When the driver inputs new information into the device, such as traffic conditions or weather changes, the information is immediately sent to the server. For example, if the driver inputs "Delay due to road construction," the information is sent to the server.

[0070] Step 7:

[0071] The server will re-analyze the route based on the new data.

[0072] The server re-analyzes the data based on the feedback sent by the driver. This re-analysis reflects the latest traffic and weather information and re-generates an optimized route. For example, if road construction is reported, the server will suggest a new detour route.

[0073] This series of processing steps provides drivers with logistics routes optimized in real time, reducing the burden on drivers and achieving efficient logistics.

[0074] Example 1

[0075] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0076] Current logistics systems have difficulty effectively utilizing real-time information on road conditions and weather to generate optimal delivery routes, resulting in increased workloads for workers and rising transportation costs. Another issue is the insufficient means for incorporating feedback from workers into the entire system, making it difficult to quickly improve routes.

[0077] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0078] In this invention, the server includes a data acquisition means for collecting road condition and weather information in real time via a digital data interface, a data analysis means for analyzing road congestion, weather fluctuations, and worker fatigue levels based on the collected data, a route optimization means for generating optimal delivery routes based on the results of the data analysis means and creating operation plans that take worker burdens into consideration, and a feedback collection means for dynamically receiving input from workers and improving routes across the entire system. This makes it possible to generate fast and efficient delivery routes based on environmental information that changes in real time, reduce worker burdens, and reduce transportation costs.

[0079] A "digital data interface" is a communication means for collecting information from various data sources in real time.

[0080] "Data acquisition means" is a function that acquires necessary information in real time from external APIs and internal systems.

[0081] "Data analysis means" refers to AI algorithms and related tools for analyzing collected data and predicting and assessing changes in the situation.

[0082] A "route optimization method" is a means for generating optimal logistics routes based on analysis results and creating operation plans that take into account the burden on workers.

[0083] The "feedback collection means" is a function that sends real-time feedback from workers to the server and uses it to reevaluate the system and improve routes.

[0084] "Display means" refers to a device that visually presents optimal routes and operation plans, as well as real-time traffic and weather information to workers.

[0085] "Communication means" refers to a means for transmitting and receiving data packets between the server and the worker's terminal.

[0086] This invention is a system that utilizes generative AI models to improve logistics efficiency and reduce the burden on workers. By linking the elements of the server, terminals, and users, this system generates optimal delivery routes that take into account changing road conditions and weather information in real time, and can adjust operation plans to reflect worker feedback.

[0087] Hardware and software used

[0088] server

[0089] The server collects various data via a digital data interface, analyzes the data using a generative AI model, and performs route optimization. Specifically, it uses the following APIs and software:

[0090] Google Maps API: Obtains real-time road condition data such as traffic volume and accident information.

[0091] OpenWeatherMap API: Get real-time weather information.

[0092] TENSORFLOW (registered trademark): A library for executing AI algorithms required for data analysis.

[0093] Terminal

[0094] The device visually displays the optimal route and operation plan sent from the server, and also provides real-time updates on traffic and weather forecasts and collects feedback from workers.

[0095] GPS navigation software: used to provide route guidance.

[0096] Real-time notification system: Notify workers of traffic and weather updates.

[0097] User (worker)

[0098] The user (worker) drives the vehicle according to the instructions displayed on the terminal and inputs feedback on road conditions and weather changes into the terminal.

[0099] Program processing

[0100] The server collects real-time road and weather information via the Google Maps API and OpenWeatherMap API. It then formats the data and converts it into a consistent format. The collected data is analyzed using a generative AI model (e.g., TensorFlow). Based on the analysis results, the server generates optimal delivery routes and creates operation plans that take into account worker workloads.

[0101] The plan is sent from the server to the device, which displays it visually to the worker. Additionally, traffic and weather updates are communicated to the worker in real time through visual and audio notifications. The worker enters feedback on road conditions and weather changes into the device, which sends this information to the server. The server reanalyzes the route based on the new information and updates the trip plan as needed.

[0102] Specific examples

[0103] For example, the server retrieves traffic information using the Google Maps API and rainfall information using the OpenWeatherMap API. The collected data is analyzed using the TensorFlow library to generate a delivery plan that delivers high-priority items (e.g., medical supplies) first. The terminal displays instructions such as, "The next right turn is 2 kilometers away. At your current speed, you can reach it within 10 minutes."

[0104] Prompt Sentence Examples

[0105] You are a logistics manager. You want to use the latest technology to reduce the workload of your workers and ensure efficient deliveries. There is currently congestion on the roads, but no rain. Please suggest the following optimal route to prioritize the urgent delivery of medical supplies:

[0106] This system will enable the generation of fast and efficient delivery routes based on real-time changing environmental information, reducing the burden on workers and cutting transportation costs.

[0107] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0108] Step 1:

[0109] The server collects road and weather information in real time via a digital data interface. Specifically, it uses the Google Maps API to obtain traffic and accident information, and the OpenWeatherMap API to obtain weather information. The server obtains data by sending parameters such as an API key and location information. The input is a request to the API, and the output is the latest road and weather information.

[0110] Step 2:

[0111] The server converts the collected data into a consistent format. For example, data acquired in JSON format can be converted to CSV format and invalid data can be removed. This preprocessing step also performs missing value imputation and noise removal. The input is the collected raw data, and the output is the formatted data.

[0112] Step 3:

[0113] The server analyzes the preprocessed data using an AI algorithm (e.g., TensorFlow). Specifically, it uses an LSTM model to predict road congestion and weather fluctuations, and performs analysis that takes driver fatigue into account. The input is the formatted data, and the output is the predicted results of road congestion, weather fluctuations, and driver fatigue.

[0114] Step 4:

[0115] The server generates multiple delivery route candidates based on the analysis results. For example, it simulates multiple routes using discrete event simulation. At this stage, the server takes product characteristics (weight, volume, priority) into account and generates a route that prioritizes high-priority products. The inputs are the analysis results and product characteristic data, and the output is multiple route candidates.

[0116] Step 5:

[0117] The server evaluates the generated route candidates and selects the most efficient route that places the least strain on the driver. For example, it evaluates each route using a cost function and determines the optimal route based on parameters such as total travel time and number of rest stops. The input is the route candidates, and the output is the optimal route.

[0118] Step 6:

[0119] The server sends the optimal route and operation plan to the terminal. Specifically, it uses an HTTP POST request to send the operation plan to the terminal as a data packet. The optimal route and operation plan are input, and are sent to the terminal as output.

[0120] Step 7:

[0121] The device receives route information and visually displays it to the driver. For example, it uses a mapping app to display the route on a map and provide turn-by-turn instructions. It also notifies the driver of real-time traffic and weather updates. The input is the route information and trip plan sent from the server, and the output is presented to the driver in the form of visuals and notifications.

[0122] Step 8:

[0123] The user (driver) inputs feedback about road conditions and weather changes into the device. Specifically, feedback such as "road construction" is input using voice input or a touch screen. The input can be single or continuous feedback data, and the output is stored in the device and sent to the server.

[0124] Step 9:

[0125] The terminal immediately sends the collected feedback to the server, for example, by sending the feedback data to the " / feedback / update" endpoint via an HTTP POST request. The input is the feedback data from the driver, and the output is sent to the server.

[0126] Step 10:

[0127] The server reanalyzes the route based on the new feedback information and updates it as necessary. Specifically, it analyzes the feedback information and again uses an AI algorithm to recalculate the optimal route. The input is the new feedback information, and the output is an updated optimal route. This new route is again sent to the device and notified to the driver.

[0128] (Application example 1)

[0129] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0130] Current logistics systems often do not utilize real-time information on road conditions and weather, resulting in frequent delivery delays due to traffic congestion and bad weather. Furthermore, operation plans are often made without taking driver fatigue into account, leaving drivers with no improvement in their working conditions. Furthermore, there is a lack of systems that can immediately respond to changes in road conditions and weather that differ from the current situation, making efficient delivery difficult.

[0131] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0132] In this invention, the server includes a data collection means for collecting road conditions and weather information in real time, a data analysis means for analyzing road congestion, weather fluctuations, and driver fatigue based on the collected data, and a route optimization means for generating an optimal logistics route based on the analysis results and creating an operation plan that takes driver workload into consideration. This makes it possible to propose efficient and rapid logistics routes and reduce driver workload.

[0133] "Data collection means for collecting road conditions and weather information in real time" refers to a device or program for continuously obtaining current road conditions and weather information from an external API, etc.

[0134] "Data analysis means for analyzing road congestion, weather changes, and driver fatigue level based on collected data" refers to a device or program for analyzing collected information and evaluating the current state of road congestion, weather changes, and driver fatigue level.

[0135] "Route optimization means for generating optimal logistics routes based on analysis results and creating operation plans that take driver workload into consideration" refers to a device or program that generates routes and operation plans to optimize delivery efficiency and the driver's working environment based on analyzed data.

[0136] The "feedback receiving means for receiving input of information while driving" is a device or program for collecting feedback information (such as changes in road conditions and weather) from the driver.

[0137] The "re-route generation means for reanalyzing and re-proposing an optimal route based on input information" is a device or program for calculating and proposing a new optimal route based on feedback information from the driver.

[0138] "Display means for displaying the optimal route and operation plan to the driver and notifying him of updates to traffic information and weather forecasts in real time" refers to a device or program that displays the generated route and operation plan, as well as the latest traffic and weather information, in an easy-to-understand manner to the driver.

[0139] The "feedback collection means for collecting feedback from the driver and transmitting it to a server to improve the route" is a device or program for collecting feedback information from the driver and transmitting the information to a server to generate a new optimal route.

[0140] Overall system overview

[0141] This invention is a system that uses a generative AI model to propose optimal logistics routes in real time. The system consists of a server, terminals, and users, all of which work in conjunction with each other. The detailed roles and operations of each component are explained below.

[0142] Server Operation

[0143] Data collection

[0144] The server collects data using the following external APIs:

[0145] Road information: Real-time road conditions are collected from external map service APIs (general name: map information provision APIs).

[0146] Weather information: Obtain real-time weather information from a weather information API.

[0147] Operation management system: Obtains cargo information, driver work status, and vehicle status from logistics management.

[0148] Examples:

[0149] Road information is obtained from the map information API, and weather information is obtained from the weather information API.

[0150] The collected information is stored in the server's disk storage.

[0151] Data analysis

[0152] A generative AI model is used to analyze the collected data.

[0153] Congestion analysis: Evaluate the degree of congestion on roads.

[0154] Weather Variability Analysis: Assess current and forecasted weather.

[0155] Fatigue analysis: Evaluate the driver's fatigue level based on their work status.

[0156] Examples:

[0157] The driver's fatigue level is evaluated from the driving status log.

[0158] A generative AI model analyzes data on congestion and weather fluctuations to generate a delivery plan that prioritizes high-priority items.

[0159] Route Optimization

[0160] Route optimization tools are used to generate optimal routes and operation plans based on the collected and analyzed data.

[0161] Route generation: A generative AI model suggests the optimal route.

[0162] Rest Point Suggestion: Suggest appropriate rest points to avoid long driving times.

[0163] Examples:

[0164] The server suggests the optimal route while taking into account the driver's level of fatigue.

[0165] The generated route includes plans to use expressways.

[0166] Device behavior

[0167] Displaying instructions

[0168] The terminal displays the optimal route and operation plan sent from the server to the driver.

[0169] Real-time notifications: Traffic and weather updates and notifications in real time.

[0170] Examples:

[0171] Specific instructions such as "The next right turn is 2 kilometers away. At your current speed, you can get there in 10 minutes" are displayed on the device.

[0172] Collecting feedback

[0173] Feedback from drivers is collected and sent to the server to improve routes.

[0174] Information input: The driver inputs changes in road conditions and weather.

[0175] Server submission: The input information is immediately sent to the server for re-analysis.

[0176] Examples:

[0177] If a driver feels they are stuck in traffic, they can enter "Stuck in Traffic."

[0178] Based on the input information, a new optimal route is generated by the server and sent back to the terminal.

[0179] User Actions

[0180] Check the route

[0181] The user (driver) checks the route displayed on the terminal and starts driving according to the instructions.

[0182] Examples:

[0183] "The user follows the instructions on the device to turn right and proceed to their next destination."

[0184] Providing Feedback

[0185] Users can input changes in traffic conditions and weather into their devices and have the route revised by sending the information to the server.

[0186] Examples:

[0187] "The user inputs into the device that the road is congested, and the data is sent to the server."

[0188] Prompt Sentence Examples

[0189] Below are some example prompts to be input to the AI ​​model.

[0190] The user's current location is [latitude, longitude] and their destination is [latitude, longitude]. The following road information was obtained from the map information API: ... The following weather information was obtained from the weather information API: ... Please suggest a route for a high-priority delivery based on the current road conditions and weather.

[0191] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0192] Step 1:

[0193] The server collects real-time road conditions and weather information from external APIs. Specifically, it obtains road congestion data from a map information API and weather data from a weather information API. The obtained data is stored in a database. The input is traffic and weather information from the API, and the output is the stored data.

[0194] Step 2:

[0195] The server uses a generative AI model to analyze the collected data. First, it evaluates road congestion, then analyzes weather fluctuations, and finally evaluates the driver's work status and fatigue level. The input is the stored traffic and weather information, and the output is the congestion level of each road, weather fluctuations, and driver fatigue level assessment results.

[0196] Step 3:

[0197] The server uses a generative AI model based on the analysis results to generate the optimal logistics route. It also creates an operation plan that takes into account the driver's workload. This involves generating a route that takes into account the priority of deliveries and includes necessary rest stops. The inputs are the analysis results and delivery information, and the output is the optimal route and operation plan.

[0198] Step 4:

[0199] The terminal receives the optimal route and operation plan sent from the server and displays them to the driver. It also notifies the driver of real-time updates on traffic information and weather forecasts. Specifically, it displays instructions such as "2 kilometers to the next right turn." The input is the optimal route and operation plan, and the output is the notification to the driver.

[0200] Step 5:

[0201] When the driver inputs changes in road conditions or weather into the terminal while driving, the terminal sends that feedback information to the server. The input is feedback information from the driver, and the output is the transmission of that information to the server.

[0202] Step 6:

[0203] Based on the received feedback, the server reanalyzes the route using a generative AI model and proposes a new optimal route. This information is sent to the device in real time. The input is the feedback, and the output is the reanalyzed new optimal route.

[0204] Step 7:

[0205] The terminal receives updated route information from the server and displays the new route to the driver. The input is the new optimal route information, and the output is the new notification content to the driver.

[0206] Step 8:

[0207] The user (driver) then starts driving again according to the new route and inputs further feedback information into the terminal as necessary. This allows the driver and the system to work together to achieve efficient logistics. The input is the new route information and the driver's feedback, and the output is route optimization by the system and improvements to the driver's driving.

[0208] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0209] This invention is a new logistics system that utilizes generative AI to collect road and weather information in real time and propose optimal logistics routes based on that information. Furthermore, to reduce the driver's workload and achieve efficient logistics, it incorporates an emotion engine that recognizes the user's emotions. This system analyzes the driver's emotional state and reflects this in the operation plan, resulting in more precise route optimization.

[0210] Server Operation

[0211] Data collection

[0212] The server obtains real-time road and weather information from external APIs. This allows it to collect current traffic volume, accident information, weather conditions, and more. For example, the server uses the Google Maps API to collect traffic congestion information and the OpenWeatherMap API to obtain weather data. It also obtains cargo information, driver work status, and vehicle status from the logistics management system.

[0213] Data analysis

[0214] The collected information is analyzed on a server. AI algorithms are used to evaluate road congestion, weather fluctuations, and driver fatigue. Delivery priorities are also determined taking into account product characteristics (weight, volume, priority). For example, an AI model analyzes road congestion and weather fluctuations and adjusts delivery schedules to deliver products with high delivery priorities first.

[0215] Route Optimization

[0216] Based on the analysis results, the server generates an optimal logistics route. The generated route takes into account the driver's workload and includes rest stops and a schedule that prevents continuous driving of less than five hours. For example, it suggests a detour route that uses the highway to avoid traffic jams.

[0217] Device behavior

[0218] Displaying instructions

[0219] The device displays the optimized route and operation plan sent from the server to the driver. It also notifies the driver of real-time traffic and weather updates. For example, it displays specific instructions such as "The next right turn is 2 kilometers away. At your current speed, you can reach it within 10 minutes."

[0220] Collecting feedback

[0221] The device collects feedback from the driver. When the driver inputs changes in road conditions or weather into the device, the information is immediately sent to the server. For example, if the driver inputs "Delay due to road construction," the information is sent to the server.

[0222] Emotion Engine Operation

[0223] Emotion recognition

[0224] The device is equipped with an emotion engine that recognizes the driver's emotional state in real time. Using cameras, microphones, and various sensors, it reads emotions from the driver's facial expressions and tone of voice. For example, if the driver is feeling stressed, that information will be detected by the device.

[0225] Emotion Analysis

[0226] The server analyzes the emotion data sent from the device and evaluates the driver's stress and fatigue level. The evaluation results are used to adjust the operation plan. For example, if the emotion analysis shows that the driver is feeling highly stressed, the planned route can be changed and rest stops added.

[0227] User Actions

[0228] Check the route

[0229] The user (driver) checks the route displayed on the terminal and starts driving according to the instructions. For example, the user drives the car from the starting point based on the displayed route.

[0230] Providing Feedback

[0231] Users input information about traffic conditions, weather changes, and emotional states into the device. If there is new information, they input it into the device and the data is sent to the server. For example, if you input "the roads are congested" into the device, the information is immediately sent to the server.

[0232] This system reduces the workload of drivers while providing optimal logistics routes in real time, achieving efficient logistics and cost reduction. In addition, the emotion engine recognizes the driver's emotional state and helps adjust operation plans, providing a safer and more comfortable working environment.

[0233] The processing flow will be explained below.

[0234] Step 1:

[0235] The server collects road conditions and weather information in real time.

[0236] The server periodically obtains current traffic and weather information using external APIs. For example, the server uses the Google Maps API to collect traffic and accident information, and the OpenWeatherMap API to obtain current weather information. The server also obtains cargo information and driver work status from the Warehouse Management System (WMS) and the Transportation Management System (TMS).

[0237] Step 2:

[0238] The server performs analysis based on the collected data.

[0239] The data collected on the server is analyzed using an AI model. The analysis evaluates road congestion, weather fluctuations, and driver fatigue. Delivery priorities are also determined, taking into account product characteristics (weight, volume, priority). For example, the AI ​​model analyzes road congestion and weather fluctuations and determines if detours are necessary.

[0240] Step 3:

[0241] The server generates optimal routes and operation plans.

[0242] Based on the results of the data analysis, the server generates an optimal logistics route that takes into account the driver's workload. This generated route includes a schedule that limits rest stops and continuous driving time. For example, it suggests routes that use highways to avoid traffic jams and plans to insert a 20-minute break after three hours of driving.

[0243] Step 4:

[0244] The terminal displays the optimal route and operation plan received from the server to the driver.

[0245] The device receives data from the server and displays the optimal route and trip plan to the driver. It also provides real-time updates on traffic and weather forecasts. For example, the device might display, "The next right turn is 2 kilometers away, and you can reach it in 10 minutes at your current speed."

[0246] Step 5:

[0247] The user checks the displayed route and starts driving.

[0248] The user (driver) checks the route displayed on the terminal and starts driving according to the instructions. Specifically, the driver drives the vehicle from the departure point to the destination based on the displayed route.

[0249] Step 6:

[0250] The device recognizes the driver's emotions in real time.

[0251] The device uses cameras, microphones, and various sensors to read the driver's emotions from their facial expressions and tone of voice. The emotion engine determines the driver's stress level and fatigue. For example, the device can use the driver's facial recognition camera to detect when the driver is tired.

[0252] Step 7:

[0253] The server performs analysis based on the emotion data.

[0254] The server receives and analyzes the emotion data sent from the device. The server evaluates the driver's stress level and fatigue level and adjusts the trip plan based on the results. For example, if the driver is in a high-stress state, the server will modify the existing trip plan to include additional breaks.

[0255] Step 8:

[0256] The device collects feedback from the driver.

[0257] The device accepts feedback from the driver. When the driver inputs information about road conditions, weather changes, or a new emotional state, that information is immediately sent to the server. For example, if the driver inputs "Delay due to road construction" into the device, that information is sent to the server and reanalyzed.

[0258] Step 9:

[0259] The server will re-analyze the route based on the new data.

[0260] The server then re-analyzes the data based on the feedback and emotion data sent by the driver. This re-analysis reflects the latest traffic and weather information, as well as the driver's emotional state, and re-generates an optimized route. For example, if road construction is reported, the server will suggest a new detour route and set additional rest stops according to the driver's stress level.

[0261] This series of processing steps provides drivers with optimized logistics routes in real time, reducing their workload and achieving efficient logistics. Furthermore, the emotion engine monitors the driver's emotional state in real time and reflects this in the operation plan, ensuring driver safety and effective delivery.

[0262] Example 2

[0263] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0264] Conventional logistics systems often generate inefficient operation plans because they are unable to properly consider changes in road conditions and weather information, as well as driver fatigue levels. It is also difficult to grasp the emotional state of the driver, making it difficult to achieve safe operation and reduce driver burden. There is a need to solve these problems and provide efficient and safe operation plans.

[0265] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0266] In this invention, the server includes a data collection means for collecting road condition and weather information in real time, a data analysis means for analyzing road congestion, weather fluctuations, and driver fatigue based on the collected data, a route optimization means for generating an optimal logistics route based on the analysis results and creating an operation plan that takes driver workload into consideration, and an emotion analysis means for analyzing the driver's emotional state and reflecting it in the operation plan. This makes it possible to generate an efficient and safe operation plan that comprehensively takes into consideration road conditions, weather, and the driver's condition and emotion.

[0267] "Data collection means" means devices and methods for obtaining real-time road condition and weather information from external sources.

[0268] "Data analysis means" refers to a method and device that uses AI algorithms to analyze road congestion, weather fluctuations, and driver fatigue levels based on collected road and weather information.

[0269] The "route optimization means" is a method and device for generating an optimal logistics route and creating an operation plan while taking into consideration the driver's workload, based on the analysis results obtained by the data analysis means.

[0270] The "emotion analysis means" is a method and device for monitoring and analyzing the driver's emotional state and reflecting it in the operation plan.

[0271] "Display means" refers to a device and method that displays optimal routes and operation plans to drivers and notifies them of traffic information and weather forecast updates in real time.

[0272] "Feedback collection means" refers to devices and methods for collecting driver feedback and transmitting it to a server for route improvement.

[0273] This system collects road and weather information in real time and proposes optimal logistics routes based on that information. Furthermore, it analyzes the driver's emotional state and reflects it in the operation plan, achieving more precise route optimization.

[0274] Server Operation

[0275] Data collection methods

[0276] The server uses the Google Maps API to obtain real-time road information, which allows it to collect traffic volume, accident information, and congestion information. It also uses the OpenWeatherMap API to obtain weather data and obtain current weather and forecasts. Furthermore, it collects vehicle load information, driver work status, and vehicle condition information from the logistics management system.

[0277] Data Analysis Methods

[0278] The server uses collected road and weather information to perform data analysis using AI algorithms. Specifically, it evaluates road congestion, accident rates, and weather fluctuations. It determines delivery priorities by taking into account the driver's work schedule, vehicle condition, and cargo information.

[0279] Route optimization methods

[0280] The server then generates optimal logistics routes based on the results of the data analysis. These routes take into account the driver's workload and include rest stops and a schedule that limits continuous driving to five hours. For example, it suggests detour routes, including using highways to avoid traffic jams.

[0281] Emotion analysis means

[0282] The server analyzes the driver's emotional data, including facial expressions and tone of voice, sent from the device. This data is used to evaluate the driver's stress and fatigue level, and the results are reflected in the operation plan. Based on the results of the driver's emotional analysis, the server can increase rest stops or readjust the route as necessary.

[0283] Device behavior

[0284] Display means

[0285] The device displays the optimized route and operation plan sent from the server to the driver. It also notifies the driver of real-time traffic and weather updates while driving. For example, it displays specific instructions such as, "The next right turn is 2 kilometers away. At your current speed, you can reach it within 10 minutes."

[0286] Feedback collection methods

[0287] The device collects feedback from the driver: if the driver types in "delay due to roadworks," the information is immediately sent to the server, which recalculates the route.

[0288] User Actions

[0289] Check the route

[0290] The user checks the route displayed on the terminal and starts driving according to the instructions, for example, by moving the car from the starting point based on the displayed route.

[0291] Providing Feedback

[0292] Users input information about traffic conditions, weather changes, and their emotional state into the device. For example, if they input "the roads are congested," that information is immediately sent to the server, and the route is recalculated.

[0293] Specific examples

[0294] 1. The server uses the Google Maps API to obtain real-time traffic information.

[0295] 2. The server analyzes the collected information and the driver's work status to generate a driving plan including the optimal route and rest points.

[0296] 3. The device will display instructions to the driver saying, "The next right turn is 2 kilometers away. At your current speed, you can get there in 10 minutes."

[0297] 4. When a driver types "the road is congested" into the device, that information is immediately sent to the server and the route is recalculated.

[0298] Example prompts for generative AI models

[0299] "How can I get real-time traffic information for a specific location using the Google Maps API?"

[0300] "Please provide an example of an algorithm for recognizing emotions from a driver's facial expressions and tone of voice."

[0301] This system reduces the workload of drivers, enabling efficient logistics and cost reduction. It also uses an emotion engine to understand the driver's emotional state and use this information to adjust operation plans, providing a safer and more comfortable working environment.

[0302] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0303] Server Operation Steps

[0304] Step 1: Data collection

[0305] The server obtains real-time road information (traffic volume, accident information, congestion information) through the Google Maps API. The input is the coordinate information of a specific location, and the output is road condition data for the corresponding location. This allows the current traffic situation to be understood.

[0306] The server uses the OpenWeatherMap API to obtain weather data (current weather and forecast). The input is the coordinate information of a location, and the output is the weather information for that location. This allows us to determine the weather conditions that will affect logistics routes.

[0307] Furthermore, the server obtains information on the vehicle's cargo, the driver's work status, the vehicle's condition, etc. from the logistics management system. The input is various data related to the operation plan, and the output is a data set that aggregates this information.

[0308] Specific operation: The server sends a request to the Google Maps API every minute, collects road information, stores it in a database, and updates weather data every hour using the OpenWeatherMap API.

[0309] Step 2: Data analysis

[0310] The server analyzes the collected road and weather information using an AI algorithm. The input is the dataset collected in step 1, and the output is the analysis results regarding road congestion, accident rates, and weather fluctuations. This allows for the evaluation necessary to select logistics routes.

[0311] The server determines delivery priorities based on the driver's work schedule, vehicle status, and cargo information. The inputs are driver work schedule data, vehicle status data, and product data, and the output is a list of products with high delivery priority.

[0312] How it works: AI algorithms analyze data and calculate the optimal route based on current traffic and weather conditions, then select items with high delivery priority and plan the route accordingly.

[0313] Step 3: Route optimization

[0314] Based on the analysis results, the server generates the optimal logistics route taking into account the driver's fatigue level and stress. The input is the analysis results obtained in step 2 and the product list, and the output is the optimized logistics route.

[0315] The route will include rest stops and a schedule that limits continuous operation to five hours, allowing for safe and efficient operation planning.

[0316] Specific operation: Based on the results of the driver's emotion analysis, the server reevaluates the planned route and adds rest stops as necessary. To avoid traffic jams, the server suggests alternative routes, including using the highway.

[0317] Device operation steps

[0318] Step 1: View instructions

[0319] The terminal displays the optimal route and operation plan sent from the server to the driver. The input is the operation plan data from the server, and the output is a display of specific instructions to the driver.

[0320] It provides real-time traffic and weather updates while driving, keeping drivers up to date with the latest information.

[0321] Specific actions: Specific instructions are displayed, such as "The next right turn is 2 kilometers away. At your current speed, you can reach it within 10 minutes." The device also notifies you in real time, "Heavy rain is currently forecast ahead. Please drive safely."

[0322] Step 2: Gather feedback

[0323] The terminal collects feedback from the driver. The input is the feedback information that the driver enters into the terminal, and the output is the process of sending that information to the server.

[0324] For example, if you enter "Delay due to road construction," that information is immediately sent to the server and the route is recalculated.

[0325] Specific operation: When the driver inputs into the device, "The current road conditions are more congested than expected," the device immediately sends that information to the server, which then recalculates the route.

[0326] Emotion Engine Operation Steps

[0327] Step 1: Recognize emotions

[0328] The device uses a camera, microphone, and various sensors to recognize the driver's emotional state in real time. The input is emotional data from the camera and microphone, and the output is the display of the emotional data on the device.

[0329] It assesses the driver's real-time emotional state by reading emotions from their facial expressions and tone of voice.

[0330] How it works: The camera detects signs of stress from the driver's facial expression, and the microphone detects fatigue from the driver's tone of voice.

[0331] Step 2: Sentiment Analysis

[0332] The server analyzes the emotion data sent from the terminal and evaluates the driver's stress and fatigue level. The input is emotion data, and the output is the analyzed emotion evaluation result.

[0333] The results of the evaluation are used to adjust the operation plan and make necessary changes based on the driver's condition.

[0334] Specific behavior: If the server determines that the driver's stress level is high, it will immediately recalculate the route and increase the number of rest points.

[0335] User action steps

[0336] Step 1: Verify the route

[0337] The user checks the route displayed on the terminal and starts driving according to the instructions. The input is the operation plan displayed on the terminal, and the output is the specific actions to be taken.

[0338] Efficient operation is possible by having the vehicle proceed from the starting point based on the displayed route.

[0339] Specific operation: The user follows the instructions on the device and proceeds along the displayed route.

[0340] Step 2: Provide feedback

[0341] The user inputs information about traffic conditions, weather changes, and emotional state into the terminal. The input is the feedback information that the driver inputs into the terminal, and the output is the process of sending that information to the server.

[0342] For example, if you enter "the road is congested," that information is immediately sent to the server and the route is recalculated.

[0343] Specific operation: When the user types "The weather has suddenly changed and it has started to snow" into the device, that information is sent to the server, which then recalculates the route.

[0344] Example prompts for generative AI models

[0345] "How can I get real-time traffic information for a specific location using the Google Maps API?"

[0346] "Please provide an example of an algorithm for recognizing emotions from a driver's facial expressions and tone of voice."

[0347] (Application example 2)

[0348] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0349] In modern logistics systems, it is important to generate optimal logistics routes by responding to real-time changes in road conditions and weather information. Furthermore, to reduce driver workload and achieve efficient delivery, it is necessary to recognize the driver's emotional state and reflect it in the operation plan. However, conventional systems that process these factors in an integrated manner lack real-time information updates and driver emotional analysis, making it difficult to provide a safe and efficient working environment.

[0350] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0351] In this invention, the server includes a data collection means for collecting road condition and weather information in real time, a data analysis means for analyzing road congestion, weather fluctuations, and driver fatigue levels based on the collected data, a route optimization means for generating an optimal logistics route based on the analysis results and creating an operation plan that takes driver workload into consideration, an emotion recognition means for recognizing and analyzing the emotional state of the driver, and an operation plan adjustment means for adjusting the operation plan based on the emotional state of the driver. This enables real-time information updates and driver emotion analysis, making it possible to realize a safe and efficient logistics system.

[0352] "Data collection means" means a device or system that collects road condition and weather information in real time.

[0353] "Data analysis means" refers to a device or system that analyzes road congestion, weather fluctuations, and driver fatigue based on collected data.

[0354] "Route optimization means" refers to a device or system that generates optimal logistics routes based on the results of data analysis and creates operation plans that take driver workload into consideration.

[0355] "Emotion recognition means" refers to a device or system for recognizing and analyzing the emotional state of a driver.

[0356] "Operation plan adjustment means" refers to a device or system that adjusts the operation plan based on the emotional state of the driver.

[0357] "Display means" refers to a device or system that displays the optimum route and operation plan to the driver and notifies them of traffic information and weather forecast updates in real time.

[0358] "Feedback collection means" refers to a device or system that collects feedback from drivers and transmits it to a server to improve the route.

[0359] This invention is a new logistics system that utilizes generative AI, which collects road and weather information in real time and proposes optimal logistics routes based on that information. Furthermore, to reduce the burden on drivers and achieve efficient logistics, it combines an emotion engine that recognizes the user's emotions. To realize this system, it has the following functions:

[0360] Server Operation

[0361] Data collection

[0362] The server obtains real-time road and weather information from external APIs. This allows it to collect current traffic volume, accident information, weather conditions, and more. Specifically, the server uses Google Maps API to collect traffic congestion information and OpenWeatherMap API to obtain weather data. It also obtains cargo information, driver work status, and vehicle status from the logistics management system.

[0363] Data analysis

[0364] The collected information is analyzed on a server. AI algorithms are used to evaluate road congestion, weather fluctuations, and driver fatigue. Delivery priorities are also determined taking into account product characteristics (weight, volume, priority). For example, an AI model analyzes road congestion and weather fluctuations and adjusts delivery schedules to deliver products with high delivery priorities first.

[0365] Route Optimization

[0366] Based on the analysis results, the server generates an optimal logistics route. The generated route takes into account the driver's workload and includes rest stops and a schedule that prevents continuous driving of less than five hours. For example, it suggests a detour route that uses the highway to avoid traffic jams.

[0367] Device behavior

[0368] Displaying instructions

[0369] The device displays the optimized route and operation plan sent from the server to the driver. It also notifies the driver of real-time traffic and weather updates. For example, it displays specific instructions such as "The next right turn is 2 kilometers away. At your current speed, you can reach it within 10 minutes."

[0370] Collecting feedback

[0371] The device collects feedback from the driver. When the driver inputs changes in road conditions or weather into the device, the information is immediately sent to the server. For example, if the driver inputs "Delay due to road construction," the information is sent to the server.

[0372] Emotion Engine Operation

[0373] Emotion recognition

[0374] The device is equipped with an emotion engine that recognizes the driver's emotional state in real time. Using cameras, microphones, and various sensors, it reads emotions from the driver's facial expressions and tone of voice. For example, if the driver is feeling stressed, that information will be detected by the device.

[0375] Emotion Analysis

[0376] The server analyzes the emotion data sent from the device and evaluates the driver's stress and fatigue level. The evaluation results are used to adjust the operation plan. For example, if the emotion analysis shows that the driver is feeling highly stressed, the planned route can be changed and rest stops added.

[0377] Hardware and software used

[0378] The main hardware used is a smartphone, smart glasses, a head-mounted display, and a server, while the software used includes Google Maps API, OpenWeatherMap API, Emotion API, TensorFlow, React Native, and Node.js.

[0379] Examples and prompts

[0380] For example, for emotion recognition, the following prompt sentences are used:

[0381] Example prompt sentence:

[0382] "If a driver is feeling stressed, identify that emotion in real time and adjust your operation plan."

[0383] Example of a prompt for real-time route generation:

[0384] "Generate the most efficient and safe route, taking into account your current location and weather information."

[0385] This system reduces the burden on drivers while providing optimal logistics routes in real time, achieving efficient logistics and cost reductions. In addition, the emotion engine recognizes the emotional state of drivers and uses this information to adjust operation plans, providing a safer and more comfortable working environment.

[0386] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0387] Step 1:

[0388] The server collects road conditions and weather information in real time. It uses the Google Maps API to obtain traffic information (e.g., traffic congestion information) and the OpenWeatherMap API to obtain the latest weather data. Specifically, it sends the current coordinates as input to each API and stores the obtained traffic and weather information in an internal database.

[0389] Input: Current coordinates

[0390] Output: Latest traffic information, weather data

[0391] Step 2:

[0392] The server uses AI algorithms to analyze road congestion and weather fluctuations based on the collected traffic information and weather data. It also analyzes driver fatigue levels and vehicle conditions obtained from the logistics management system. This allows the AI ​​model to combine each piece of data to create basic data for generating optimal logistics routes.

[0393] Input: Traffic information, weather data, driver fatigue level, vehicle condition

[0394] Output: Analysis results (road congestion, weather fluctuations, driver status)

[0395] Step 3:

[0396] The server generates optimal logistics routes based on the analysis results and creates operation plans that take driver workloads into account. Specifically, the schedule is set taking into account driver rest stops and continuous driving time. The generated operation plan also includes the priority of the next delivery and detour routes.

[0397] Input: Analysis results

[0398] Output: Optimal logistics routes and operation plans

[0399] Step 4:

[0400] The device displays the optimal route and operation plan sent from the server to the driver. The screen also displays the distance to the next right turn and the arrival time based on speed. It also has a function to notify the driver of traffic information and weather forecast updates in real time.

[0401] Input: Optimal logistics route, operation plan

[0402] Output: Display to driver (route guidance, traffic information, weather forecast)

[0403] Step 5:

[0404] The device collects feedback from the driver. When the driver inputs information about road conditions and weather changes into the device, the data is immediately sent to the server. This feedback information is used by the server to improve the route.

[0405] Input: Driver feedback (road conditions, weather changes)

[0406] Output: Send to server, collect feedback information

[0407] Step 6:

[0408] The device's emotion recognition engine recognizes the driver's emotional state in real time. Specifically, it uses the camera and microphone in the smart glasses or head-mounted display to analyze the driver's facial expressions and tone of voice to determine their stress and fatigue levels. This information is recorded on the device as emotion recognition data and sent to a server.

[0409] Input: Driver's facial expression data, voice tone

[0410] Output: Emotion recognition data (stress, fatigue level)

[0411] Step 7:

[0412] The server analyzes the emotion recognition data sent from the device and evaluates the driver's stress and fatigue level. Based on this evaluation result, the operation plan is adjusted. For example, if the driver is feeling very stressed, the planned route may be changed and adjustments made such as increasing rest stops.

[0413] Input: Emotion recognition data

[0414] Output: Adjusted trip plan

[0415] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0416] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0417] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0418] [Second embodiment]

[0419] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0420] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0421] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0422] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0423] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0424] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0425] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0426] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0427] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0428] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0429] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0430] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0431] This invention is a new logistics system that utilizes generative AI. This system collects road and weather information in real time and can quickly propose optimal logistics routes. It also provides plans to minimize driver workload, aiming to reduce transportation costs and improve driver working conditions.

[0432] Server Operation

[0433] Data collection

[0434] The server obtains real-time road and weather information from external APIs. This allows it to collect current traffic volume, accident information, weather conditions, etc. It also obtains cargo information, driver work status, and vehicle status from the logistics management system. For example, the server obtains road information from the Google Maps API and weather information from the OpenWeatherMap API, while simultaneously understanding the driver's work status from the traffic management system.

[0435] Data analysis

[0436] The collected information is analyzed on a server. An AI algorithm is used to analyze the collected data and evaluate road congestion, weather fluctuations, and driver fatigue. Delivery priorities are also determined taking into account product characteristics (weight, volume, priority, etc.). For example, an AI model analyzes road congestion and weather fluctuations to generate a delivery plan that prioritizes the delivery of high-priority products.

[0437] Route Optimization

[0438] Based on the analysis results, the server generates an optimal logistics route. The generated route takes into account the driver's workload, avoids long periods of continuous driving, and includes regular rest stops. For example, the server may suggest detour routes to avoid traffic jams and create a plan for efficient delivery by using expressways.

[0439] Device behavior

[0440] Displaying instructions

[0441] The device displays the optimized route and operation plan sent from the server to the driver. It also notifies the driver of traffic information and weather forecast updates in real time. For example, the device displays specific instructions such as "The next right turn is 2 kilometers away. At your current speed, you can reach it within 10 minutes."

[0442] Collecting feedback

[0443] The device collects feedback from the driver. When the driver inputs road conditions or changes in weather into the device, that information is immediately sent to the server, and the route is improved. For example, if the driver inputs "delays due to road construction" into the device, that information is sent to the server and reanalyzed.

[0444] User Actions

[0445] Check the route

[0446] The user (driver) checks the route displayed on the device and starts driving according to the instructions. For example, the user follows the instructions on the device to turn right and proceed to the next destination.

[0447] Providing Feedback

[0448] Users input changes in traffic conditions and weather into their devices. If there is new information, it is immediately entered into the device and sent to the server, which then revises the route. For example, "the user can input that the road is congested into the device, and that data is sent to the server, which then generates a new optimal route."

[0449] This system will reduce the burden on drivers while improving logistics efficiency and reducing costs.

[0450] The processing flow will be explained below.

[0451] Step 1:

[0452] The server collects road conditions and weather information in real time.

[0453] The server periodically obtains current traffic and weather information using external APIs. Specifically, the server uses the Google Maps API to collect road congestion and accident information, and the OpenWeatherMap API to obtain current weather information. The server also obtains cargo information and driver work status from the logistics management system (WMS) and the transportation management system (TMS).

[0454] Step 2:

[0455] The server performs analysis based on the collected data.

[0456] The data collected on the server is analyzed using an AI model. The analysis evaluates road congestion, weather fluctuations, and driver fatigue. Product characteristics (weight, volume, priority) are also taken into account to determine delivery priorities. For example, the server inputs the collected data into the AI ​​to determine whether a detour route is necessary if a particular road is congested.

[0457] Step 3:

[0458] The server generates optimal routes and operation plans.

[0459] Based on the results of the data analysis, the server generates an optimal logistics route that takes into account the driver's workload. The generated route includes rest stops for the driver and a schedule to prevent continuous driving of less than five hours. For example, the server suggests using highways or taking shorter routes to avoid traffic jams.

[0460] Step 4:

[0461] The terminal displays the optimal route and operation plan received from the server to the driver.

[0462] The device receives data from the server and displays the optimal route and trip plan to the driver. It also provides real-time updates on traffic and weather forecasts. For example, the device might display specific instructions such as, "The next right turn is 2 kilometers away. At your current speed, you can reach it within 10 minutes."

[0463] Step 5:

[0464] The user checks the displayed route and starts driving.

[0465] The user (driver) checks the route displayed on the terminal and starts driving according to the instructions, for example, driving the car towards the destination according to the clearly indicated route.

[0466] Step 6:

[0467] The device collects feedback from the driver.

[0468] The device accepts feedback from the driver. When the driver inputs new information into the device, such as traffic conditions or weather changes, the information is immediately sent to the server. For example, if the driver inputs "Delay due to road construction," the information is sent to the server.

[0469] Step 7:

[0470] The server will re-analyze the route based on the new data.

[0471] The server re-analyzes the data based on the feedback sent by the driver. This re-analysis reflects the latest traffic and weather information and re-generates an optimized route. For example, if road construction is reported, the server will suggest a new detour route.

[0472] This series of processing steps provides drivers with logistics routes optimized in real time, reducing the burden on drivers and achieving efficient logistics.

[0473] Example 1

[0474] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0475] Current logistics systems have difficulty effectively utilizing real-time information on road conditions and weather to generate optimal delivery routes, resulting in increased workloads for workers and rising transportation costs. Another issue is the insufficient means for incorporating feedback from workers into the entire system, making it difficult to quickly improve routes.

[0476] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0477] In this invention, the server includes a data acquisition means for collecting road condition and weather information in real time via a digital data interface, a data analysis means for analyzing road congestion, weather fluctuations, and worker fatigue levels based on the collected data, a route optimization means for generating optimal delivery routes based on the results of the data analysis means and creating operation plans that take worker burdens into consideration, and a feedback collection means for dynamically receiving input from workers and improving routes across the entire system. This makes it possible to generate fast and efficient delivery routes based on environmental information that changes in real time, reduce worker burdens, and reduce transportation costs.

[0478] A "digital data interface" is a communication means for collecting information from various data sources in real time.

[0479] "Data acquisition means" is a function that acquires necessary information in real time from external APIs and internal systems.

[0480] "Data analysis means" refers to AI algorithms and related tools for analyzing collected data and predicting and assessing changes in the situation.

[0481] A "route optimization method" is a means for generating optimal logistics routes based on analysis results and creating operation plans that take into account the burden on workers.

[0482] The "feedback collection means" is a function that sends real-time feedback from workers to the server and uses it to reevaluate the system and improve routes.

[0483] "Display means" refers to a device that visually presents optimal routes and operation plans, as well as real-time traffic and weather information to workers.

[0484] "Communication means" refers to a means for transmitting and receiving data packets between the server and the worker's terminal.

[0485] This invention is a system that utilizes generative AI models to improve logistics efficiency and reduce the burden on workers. By linking the elements of the server, terminals, and users, this system generates optimal delivery routes that take into account changing road conditions and weather information in real time, and can adjust operation plans to reflect worker feedback.

[0486] Hardware and software used

[0487] server

[0488] The server collects various data via a digital data interface, analyzes the data using a generative AI model, and performs route optimization. Specifically, it uses the following APIs and software:

[0489] Google Maps API: Obtains real-time road condition data such as traffic volume and accident information.

[0490] OpenWeatherMap API: Get real-time weather information.

[0491] TensorFlow: A library for running AI algorithms required for data analysis.

[0492] Terminal

[0493] The device visually displays the optimal route and operation plan sent from the server, and also provides real-time updates on traffic and weather forecasts and collects feedback from workers.

[0494] GPS navigation software: used to provide route guidance.

[0495] Real-time notification system: Notify workers of traffic and weather updates.

[0496] User (worker)

[0497] The user (worker) drives the vehicle according to the instructions displayed on the terminal and inputs feedback on road conditions and weather changes into the terminal.

[0498] Program processing

[0499] The server collects real-time road and weather information via the Google Maps API and OpenWeatherMap API. It then formats the data and converts it into a consistent format. The collected data is analyzed using a generative AI model (e.g., TensorFlow). Based on the analysis results, the server generates optimal delivery routes and creates operation plans that take into account worker workloads.

[0500] The plan is sent from the server to the device, which displays it visually to the worker. Additionally, traffic and weather updates are communicated to the worker in real time through visual and audio notifications. The worker enters feedback on road conditions and weather changes into the device, which sends this information to the server. The server reanalyzes the route based on the new information and updates the trip plan as needed.

[0501] Specific examples

[0502] For example, the server retrieves traffic information using the Google Maps API and rainfall information using the OpenWeatherMap API. The collected data is analyzed using the TensorFlow library to generate a delivery plan that delivers high-priority items (e.g., medical supplies) first. The terminal displays instructions such as, "The next right turn is 2 kilometers away. At your current speed, you can reach it within 10 minutes."

[0503] Prompt Sentence Examples

[0504] You are a logistics manager. You want to use the latest technology to reduce the workload of your workers and ensure efficient deliveries. There is currently congestion on the roads, but no rain. Please suggest the following optimal route to prioritize the urgent delivery of medical supplies:

[0505] This system will enable the generation of fast and efficient delivery routes based on real-time changing environmental information, reducing the burden on workers and cutting transportation costs.

[0506] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0507] Step 1:

[0508] The server collects road and weather information in real time via a digital data interface. Specifically, it uses the Google Maps API to obtain traffic and accident information, and the OpenWeatherMap API to obtain weather information. The server obtains data by sending parameters such as an API key and location information. The input is a request to the API, and the output is the latest road and weather information.

[0509] Step 2:

[0510] The server converts the collected data into a consistent format. For example, data acquired in JSON format can be converted to CSV format and invalid data can be removed. This preprocessing step also performs missing value imputation and noise removal. The input is the collected raw data, and the output is the formatted data.

[0511] Step 3:

[0512] The server analyzes the preprocessed data using an AI algorithm (e.g., TensorFlow). Specifically, it uses an LSTM model to predict road congestion and weather fluctuations, and performs analysis that takes driver fatigue into account. The input is the formatted data, and the output is the predicted results of road congestion, weather fluctuations, and driver fatigue.

[0513] Step 4:

[0514] The server generates multiple delivery route candidates based on the analysis results. For example, it simulates multiple routes using discrete event simulation. At this stage, the server takes product characteristics (weight, volume, priority) into account and generates a route that prioritizes high-priority products. The inputs are the analysis results and product characteristic data, and the output is multiple route candidates.

[0515] Step 5:

[0516] The server evaluates the generated route candidates and selects the most efficient route that places the least strain on the driver. For example, it evaluates each route using a cost function and determines the optimal route based on parameters such as total travel time and number of rest stops. The input is the route candidates, and the output is the optimal route.

[0517] Step 6:

[0518] The server sends the optimal route and operation plan to the terminal. Specifically, it uses an HTTP POST request to send the operation plan to the terminal as a data packet. The optimal route and operation plan are input, and are sent to the terminal as output.

[0519] Step 7:

[0520] The device receives route information and visually displays it to the driver. For example, it uses a mapping app to display the route on a map and provide turn-by-turn instructions. It also notifies the driver of real-time traffic and weather updates. The input is the route information and trip plan sent from the server, and the output is presented to the driver in the form of visuals and notifications.

[0521] Step 8:

[0522] The user (driver) inputs feedback about road conditions and weather changes into the device. Specifically, feedback such as "road construction" is input using voice input or a touch screen. The input can be single or continuous feedback data, and the output is stored in the device and sent to the server.

[0523] Step 9:

[0524] The terminal immediately sends the collected feedback to the server, for example, by sending the feedback data to the " / feedback / update" endpoint via an HTTP POST request. The input is the feedback data from the driver, and the output is sent to the server.

[0525] Step 10:

[0526] The server reanalyzes the route based on the new feedback information and updates it as necessary. Specifically, it analyzes the feedback information and again uses an AI algorithm to recalculate the optimal route. The input is the new feedback information, and the output is an updated optimal route. This new route is again sent to the device and notified to the driver.

[0527] (Application example 1)

[0528] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0529] Current logistics systems often do not utilize real-time information on road conditions and weather, resulting in frequent delivery delays due to traffic congestion and bad weather. Furthermore, operation plans are often made without taking driver fatigue into account, leaving drivers with no improvement in their working conditions. Furthermore, there is a lack of systems that can immediately respond to changes in road conditions and weather that differ from the current situation, making efficient delivery difficult.

[0530] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0531] In this invention, the server includes a data collection means for collecting road conditions and weather information in real time, a data analysis means for analyzing road congestion, weather fluctuations, and driver fatigue based on the collected data, and a route optimization means for generating an optimal logistics route based on the analysis results and creating an operation plan that takes driver workload into consideration. This makes it possible to propose efficient and rapid logistics routes and reduce driver workload.

[0532] "Data collection means for collecting road conditions and weather information in real time" refers to a device or program for continuously obtaining current road conditions and weather information from an external API, etc.

[0533] "Data analysis means for analyzing road congestion, weather changes, and driver fatigue level based on collected data" refers to a device or program for analyzing collected information and evaluating the current state of road congestion, weather changes, and driver fatigue level.

[0534] "Route optimization means for generating optimal logistics routes based on analysis results and creating operation plans that take driver workload into consideration" refers to a device or program that generates routes and operation plans to optimize delivery efficiency and the driver's working environment based on analyzed data.

[0535] The "feedback receiving means for receiving input of information while driving" is a device or program for collecting feedback information (such as changes in road conditions and weather) from the driver.

[0536] The "re-route generation means for reanalyzing and re-proposing an optimal route based on input information" is a device or program for calculating and proposing a new optimal route based on feedback information from the driver.

[0537] "Display means for displaying the optimal route and operation plan to the driver and notifying him of updates to traffic information and weather forecasts in real time" refers to a device or program that displays the generated route and operation plan, as well as the latest traffic and weather information, in an easy-to-understand manner to the driver.

[0538] The "feedback collection means for collecting feedback from the driver and transmitting it to a server to improve the route" is a device or program for collecting feedback information from the driver and transmitting the information to a server to generate a new optimal route.

[0539] Overall system overview

[0540] This invention is a system that uses a generative AI model to propose optimal logistics routes in real time. The system consists of a server, terminals, and users, all of which work in conjunction with each other. The detailed roles and operations of each component are explained below.

[0541] Server Operation

[0542] Data collection

[0543] The server collects data using the following external APIs:

[0544] Road information: Real-time road conditions are collected from external map service APIs (general name: map information provision APIs).

[0545] Weather information: Obtain real-time weather information from a weather information API.

[0546] Operation management system: Obtains cargo information, driver work status, and vehicle status from logistics management.

[0547] Examples:

[0548] Road information is obtained from the map information API, and weather information is obtained from the weather information API.

[0549] The collected information is stored in the server's disk storage.

[0550] Data analysis

[0551] A generative AI model is used to analyze the collected data.

[0552] Congestion analysis: Evaluate the degree of congestion on roads.

[0553] Weather Variability Analysis: Assess current and forecasted weather.

[0554] Fatigue analysis: Evaluate the driver's fatigue level based on their work status.

[0555] Examples:

[0556] The driver's fatigue level is evaluated from the driving status log.

[0557] A generative AI model analyzes data on congestion and weather fluctuations to generate a delivery plan that prioritizes high-priority items.

[0558] Route Optimization

[0559] Route optimization tools are used to generate optimal routes and operation plans based on the collected and analyzed data.

[0560] Route generation: A generative AI model suggests the optimal route.

[0561] Rest Point Suggestion: Suggest appropriate rest points to avoid long driving times.

[0562] Examples:

[0563] The server suggests the optimal route while taking into account the driver's level of fatigue.

[0564] The generated route includes plans to use expressways.

[0565] Device behavior

[0566] Displaying instructions

[0567] The terminal displays the optimal route and operation plan sent from the server to the driver.

[0568] Real-time notifications: Traffic and weather updates and notifications in real time.

[0569] Examples:

[0570] Specific instructions such as "The next right turn is 2 kilometers away. At your current speed, you can get there in 10 minutes" are displayed on the device.

[0571] Collecting feedback

[0572] Feedback from drivers is collected and sent to the server to improve routes.

[0573] Information input: The driver inputs changes in road conditions and weather.

[0574] Server submission: The input information is immediately sent to the server for re-analysis.

[0575] Examples:

[0576] If a driver feels they are stuck in traffic, they can enter "Stuck in Traffic."

[0577] Based on the input information, a new optimal route is generated by the server and sent back to the terminal.

[0578] User Actions

[0579] Check the route

[0580] The user (driver) checks the route displayed on the terminal and starts driving according to the instructions.

[0581] Examples:

[0582] "The user follows the instructions on the device to turn right and proceed to their next destination."

[0583] Providing Feedback

[0584] Users can input changes in traffic conditions and weather into their devices and have the route revised by sending the information to the server.

[0585] Examples:

[0586] "The user inputs into the device that the road is congested, and the data is sent to the server."

[0587] Prompt Sentence Examples

[0588] Below are some example prompts to be input to the AI ​​model.

[0589] The user's current location is [latitude, longitude] and their destination is [latitude, longitude]. The following road information was obtained from the map information API: ... The following weather information was obtained from the weather information API: ... Please suggest a route for a high-priority delivery based on the current road conditions and weather.

[0590] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0591] Step 1:

[0592] The server collects real-time road conditions and weather information from external APIs. Specifically, it obtains road congestion data from a map information API and weather data from a weather information API. The obtained data is stored in a database. The input is traffic and weather information from the API, and the output is the stored data.

[0593] Step 2:

[0594] The server uses a generative AI model to analyze the collected data. First, it evaluates road congestion, then analyzes weather fluctuations, and finally evaluates the driver's work status and fatigue level. The input is the stored traffic and weather information, and the output is the congestion level of each road, weather fluctuations, and driver fatigue level assessment results.

[0595] Step 3:

[0596] The server uses a generative AI model based on the analysis results to generate the optimal logistics route. It also creates an operation plan that takes into account the driver's workload. This involves generating a route that takes into account the priority of deliveries and includes necessary rest stops. The inputs are the analysis results and delivery information, and the output is the optimal route and operation plan.

[0597] Step 4:

[0598] The terminal receives the optimal route and operation plan sent from the server and displays them to the driver. It also notifies the driver of real-time updates on traffic information and weather forecasts. Specifically, it displays instructions such as "2 kilometers to the next right turn." The input is the optimal route and operation plan, and the output is the notification to the driver.

[0599] Step 5:

[0600] When the driver inputs changes in road conditions or weather into the terminal while driving, the terminal sends that feedback information to the server. The input is feedback information from the driver, and the output is the transmission of that information to the server.

[0601] Step 6:

[0602] Based on the received feedback, the server reanalyzes the route using a generative AI model and proposes a new optimal route. This information is sent to the device in real time. The input is the feedback, and the output is the reanalyzed new optimal route.

[0603] Step 7:

[0604] The terminal receives updated route information from the server and displays the new route to the driver. The input is the new optimal route information, and the output is the new notification content to the driver.

[0605] Step 8:

[0606] The user (driver) then starts driving again according to the new route and inputs further feedback information into the terminal as necessary. This allows the driver and the system to work together to achieve efficient logistics. The input is the new route information and the driver's feedback, and the output is route optimization by the system and improvements to the driver's driving.

[0607] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0608] This invention is a new logistics system that utilizes generative AI to collect road and weather information in real time and propose optimal logistics routes based on that information. Furthermore, to reduce the driver's workload and achieve efficient logistics, it incorporates an emotion engine that recognizes the user's emotions. This system analyzes the driver's emotional state and reflects this in the operation plan, resulting in more precise route optimization.

[0609] Server Operation

[0610] Data collection

[0611] The server obtains real-time road and weather information from external APIs. This allows it to collect current traffic volume, accident information, weather conditions, and more. For example, the server uses the Google Maps API to collect traffic congestion information and the OpenWeatherMap API to obtain weather data. It also obtains cargo information, driver work status, and vehicle status from the logistics management system.

[0612] Data analysis

[0613] The collected information is analyzed on a server. AI algorithms are used to evaluate road congestion, weather fluctuations, and driver fatigue. Delivery priorities are also determined taking into account product characteristics (weight, volume, priority). For example, an AI model analyzes road congestion and weather fluctuations and adjusts delivery schedules to deliver products with high delivery priorities first.

[0614] Route Optimization

[0615] Based on the analysis results, the server generates an optimal logistics route. The generated route takes into account the driver's workload and includes rest stops and a schedule that prevents continuous driving of less than five hours. For example, it suggests a detour route that uses the highway to avoid traffic jams.

[0616] Device behavior

[0617] Displaying instructions

[0618] The device displays the optimized route and operation plan sent from the server to the driver. It also notifies the driver of real-time traffic and weather updates. For example, it displays specific instructions such as "The next right turn is 2 kilometers away. At your current speed, you can reach it within 10 minutes."

[0619] Collecting feedback

[0620] The device collects feedback from the driver. When the driver inputs changes in road conditions or weather into the device, the information is immediately sent to the server. For example, if the driver inputs "Delay due to road construction," the information is sent to the server.

[0621] Emotion Engine Operation

[0622] Emotion recognition

[0623] The device is equipped with an emotion engine that recognizes the driver's emotional state in real time. Using cameras, microphones, and various sensors, it reads emotions from the driver's facial expressions and tone of voice. For example, if the driver is feeling stressed, that information will be detected by the device.

[0624] Emotion Analysis

[0625] The server analyzes the emotion data sent from the device and evaluates the driver's stress and fatigue level. The evaluation results are used to adjust the operation plan. For example, if the emotion analysis shows that the driver is feeling highly stressed, the planned route can be changed and rest stops added.

[0626] User Actions

[0627] Check the route

[0628] The user (driver) checks the route displayed on the terminal and starts driving according to the instructions. For example, the user drives the car from the starting point based on the displayed route.

[0629] Providing Feedback

[0630] Users input information about traffic conditions, weather changes, and emotional states into the device. If there is new information, they input it into the device and the data is sent to the server. For example, if you input "the roads are congested" into the device, the information is immediately sent to the server.

[0631] This system reduces the workload of drivers while providing optimal logistics routes in real time, achieving efficient logistics and cost reduction. In addition, the emotion engine recognizes the driver's emotional state and helps adjust operation plans, providing a safer and more comfortable working environment.

[0632] The processing flow will be explained below.

[0633] Step 1:

[0634] The server collects road conditions and weather information in real time.

[0635] The server periodically obtains current traffic and weather information using external APIs. For example, the server uses the Google Maps API to collect traffic and accident information, and the OpenWeatherMap API to obtain current weather information. The server also obtains cargo information and driver work status from the Warehouse Management System (WMS) and the Transportation Management System (TMS).

[0636] Step 2:

[0637] The server performs analysis based on the collected data.

[0638] The data collected on the server is analyzed using an AI model. The analysis evaluates road congestion, weather fluctuations, and driver fatigue. Delivery priorities are also determined, taking into account product characteristics (weight, volume, priority). For example, the AI ​​model analyzes road congestion and weather fluctuations and determines if detours are necessary.

[0639] Step 3:

[0640] The server generates optimal routes and operation plans.

[0641] Based on the results of the data analysis, the server generates an optimal logistics route that takes into account the driver's workload. This generated route includes a schedule that limits rest stops and continuous driving time. For example, it suggests routes that use highways to avoid traffic jams and plans to insert a 20-minute break after three hours of driving.

[0642] Step 4:

[0643] The terminal displays the optimal route and operation plan received from the server to the driver.

[0644] The device receives data from the server and displays the optimal route and trip plan to the driver. It also provides real-time updates on traffic and weather forecasts. For example, the device might display, "The next right turn is 2 kilometers away, and you can reach it in 10 minutes at your current speed."

[0645] Step 5:

[0646] The user checks the displayed route and starts driving.

[0647] The user (driver) checks the route displayed on the terminal and starts driving according to the instructions. Specifically, the driver drives the vehicle from the departure point to the destination based on the displayed route.

[0648] Step 6:

[0649] The device recognizes the driver's emotions in real time.

[0650] The device uses cameras, microphones, and various sensors to read the driver's emotions from their facial expressions and tone of voice. The emotion engine determines the driver's stress level and fatigue. For example, the device can use the driver's facial recognition camera to detect when the driver is tired.

[0651] Step 7:

[0652] The server performs analysis based on the emotion data.

[0653] The server receives and analyzes the emotion data sent from the device. The server evaluates the driver's stress level and fatigue level and adjusts the trip plan based on the results. For example, if the driver is in a high-stress state, the server will modify the existing trip plan to include additional breaks.

[0654] Step 8:

[0655] The device collects feedback from the driver.

[0656] The device accepts feedback from the driver. When the driver inputs information about road conditions, weather changes, or a new emotional state, that information is immediately sent to the server. For example, if the driver inputs "Delay due to road construction" into the device, that information is sent to the server and reanalyzed.

[0657] Step 9:

[0658] The server will re-analyze the route based on the new data.

[0659] The server then re-analyzes the data based on the feedback and emotion data sent by the driver. This re-analysis reflects the latest traffic and weather information, as well as the driver's emotional state, and re-generates an optimized route. For example, if road construction is reported, the server will suggest a new detour route and set additional rest stops according to the driver's stress level.

[0660] This series of processing steps provides drivers with optimized logistics routes in real time, reducing their workload and achieving efficient logistics. Furthermore, the emotion engine monitors the driver's emotional state in real time and reflects this in the operation plan, ensuring driver safety and effective delivery.

[0661] Example 2

[0662] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0663] Conventional logistics systems often generate inefficient operation plans because they are unable to properly consider changes in road conditions and weather information, as well as driver fatigue levels. It is also difficult to grasp the emotional state of the driver, making it difficult to achieve safe operation and reduce driver burden. There is a need to solve these problems and provide efficient and safe operation plans.

[0664] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0665] In this invention, the server includes a data collection means for collecting road condition and weather information in real time, a data analysis means for analyzing road congestion, weather fluctuations, and driver fatigue based on the collected data, a route optimization means for generating an optimal logistics route based on the analysis results and creating an operation plan that takes driver workload into consideration, and an emotion analysis means for analyzing the driver's emotional state and reflecting it in the operation plan. This makes it possible to generate an efficient and safe operation plan that comprehensively takes into consideration road conditions, weather, and the driver's condition and emotion.

[0666] "Data collection means" means devices and methods for obtaining real-time road condition and weather information from external sources.

[0667] "Data analysis means" refers to a method and device that uses AI algorithms to analyze road congestion, weather fluctuations, and driver fatigue levels based on collected road and weather information.

[0668] The "route optimization means" is a method and device for generating an optimal logistics route and creating an operation plan while taking into consideration the driver's workload, based on the analysis results obtained by the data analysis means.

[0669] The "emotion analysis means" is a method and device for monitoring and analyzing the driver's emotional state and reflecting it in the operation plan.

[0670] "Display means" refers to a device and method that displays optimal routes and operation plans to drivers and notifies them of traffic information and weather forecast updates in real time.

[0671] "Feedback collection means" refers to devices and methods for collecting driver feedback and transmitting it to a server for route improvement.

[0672] This system collects road and weather information in real time and proposes optimal logistics routes based on that information. Furthermore, it analyzes the driver's emotional state and reflects it in the operation plan, achieving more precise route optimization.

[0673] Server Operation

[0674] Data collection methods

[0675] The server uses the Google Maps API to obtain real-time road information, which allows it to collect traffic volume, accident information, and congestion information. It also uses the OpenWeatherMap API to obtain weather data and obtain current weather and forecasts. Furthermore, it collects vehicle load information, driver work status, and vehicle condition information from the logistics management system.

[0676] Data Analysis Methods

[0677] The server uses collected road and weather information to perform data analysis using AI algorithms. Specifically, it evaluates road congestion, accident rates, and weather fluctuations. It determines delivery priorities by taking into account the driver's work schedule, vehicle condition, and cargo information.

[0678] Route optimization methods

[0679] The server then generates optimal logistics routes based on the results of the data analysis. These routes take into account the driver's workload and include rest stops and a schedule that limits continuous driving to five hours. For example, it suggests detour routes, including using highways to avoid traffic jams.

[0680] Emotion analysis means

[0681] The server analyzes the driver's emotional data, including facial expressions and tone of voice, sent from the device. This data is used to evaluate the driver's stress and fatigue level, and the results are reflected in the operation plan. Based on the results of the driver's emotional analysis, the server can increase rest stops or readjust the route as necessary.

[0682] Device behavior

[0683] Display means

[0684] The device displays the optimized route and operation plan sent from the server to the driver. It also notifies the driver of real-time traffic and weather updates while driving. For example, it displays specific instructions such as, "The next right turn is 2 kilometers away. At your current speed, you can reach it within 10 minutes."

[0685] Feedback collection methods

[0686] The device collects feedback from the driver: if the driver types in "delay due to roadworks," the information is immediately sent to the server, which recalculates the route.

[0687] User Actions

[0688] Check the route

[0689] The user checks the route displayed on the terminal and starts driving according to the instructions, for example, by moving the car from the starting point based on the displayed route.

[0690] Providing Feedback

[0691] Users input information about traffic conditions, weather changes, and their emotional state into the device. For example, if they input "the roads are congested," that information is immediately sent to the server, and the route is recalculated.

[0692] Specific examples

[0693] 1. The server uses the Google Maps API to obtain real-time traffic information.

[0694] 2. The server analyzes the collected information and the driver's work status to generate a driving plan including the optimal route and rest points.

[0695] 3. The device will display instructions to the driver saying, "The next right turn is 2 kilometers away. At your current speed, you can get there in 10 minutes."

[0696] 4. When a driver types "the road is congested" into the device, that information is immediately sent to the server and the route is recalculated.

[0697] Example prompts for generative AI models

[0698] "How can I get real-time traffic information for a specific location using the Google Maps API?"

[0699] "Please provide an example of an algorithm for recognizing emotions from a driver's facial expressions and tone of voice."

[0700] This system reduces the workload of drivers, enabling efficient logistics and cost reduction. It also uses an emotion engine to understand the driver's emotional state and use this information to adjust operation plans, providing a safer and more comfortable working environment.

[0701] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0702] Server Operation Steps

[0703] Step 1: Data collection

[0704] The server obtains real-time road information (traffic volume, accident information, congestion information) through the Google Maps API. The input is the coordinate information of a specific location, and the output is road condition data for the corresponding location. This allows the current traffic situation to be understood.

[0705] The server uses the OpenWeatherMap API to obtain weather data (current weather and forecast). The input is the coordinate information of a location, and the output is the weather information for that location. This allows us to determine the weather conditions that will affect logistics routes.

[0706] Furthermore, the server obtains information on the vehicle's cargo, the driver's work status, the vehicle's condition, etc. from the logistics management system. The input is various data related to the operation plan, and the output is a data set that aggregates this information.

[0707] Specific operation: The server sends a request to the Google Maps API every minute, collects road information, stores it in a database, and updates weather data every hour using the OpenWeatherMap API.

[0708] Step 2: Data analysis

[0709] The server analyzes the collected road and weather information using an AI algorithm. The input is the dataset collected in step 1, and the output is the analysis results regarding road congestion, accident rates, and weather fluctuations. This allows for the evaluation necessary to select logistics routes.

[0710] The server determines delivery priorities based on the driver's work schedule, vehicle status, and cargo information. The inputs are driver work schedule data, vehicle status data, and product data, and the output is a list of products with high delivery priority.

[0711] How it works: AI algorithms analyze data and calculate the optimal route based on current traffic and weather conditions, then select items with high delivery priority and plan the route accordingly.

[0712] Step 3: Route optimization

[0713] Based on the analysis results, the server generates the optimal logistics route taking into account the driver's fatigue level and stress. The input is the analysis results obtained in step 2 and the product list, and the output is the optimized logistics route.

[0714] The route will include rest stops and a schedule that limits continuous operation to five hours, allowing for safe and efficient operation planning.

[0715] Specific operation: Based on the results of the driver's emotion analysis, the server reevaluates the planned route and adds rest stops as necessary. To avoid traffic jams, the server suggests alternative routes, including using the highway.

[0716] Device operation steps

[0717] Step 1: View instructions

[0718] The terminal displays the optimal route and operation plan sent from the server to the driver. The input is the operation plan data from the server, and the output is a display of specific instructions to the driver.

[0719] It provides real-time traffic and weather updates while driving, keeping drivers up to date with the latest information.

[0720] Specific actions: Specific instructions are displayed, such as "The next right turn is 2 kilometers away. At your current speed, you can reach it within 10 minutes." The device also notifies you in real time, "Heavy rain is currently forecast ahead. Please drive safely."

[0721] Step 2: Gather feedback

[0722] The terminal collects feedback from the driver. The input is the feedback information that the driver enters into the terminal, and the output is the process of sending that information to the server.

[0723] For example, if you enter "Delay due to road construction," that information is immediately sent to the server and the route is recalculated.

[0724] Specific operation: When the driver inputs into the device, "The current road conditions are more congested than expected," the device immediately sends that information to the server, which then recalculates the route.

[0725] Emotion Engine Operation Steps

[0726] Step 1: Recognize emotions

[0727] The device uses a camera, microphone, and various sensors to recognize the driver's emotional state in real time. The input is emotional data from the camera and microphone, and the output is the display of the emotional data on the device.

[0728] It assesses the driver's real-time emotional state by reading emotions from their facial expressions and tone of voice.

[0729] How it works: The camera detects signs of stress from the driver's facial expression, and the microphone detects fatigue from the driver's tone of voice.

[0730] Step 2: Sentiment Analysis

[0731] The server analyzes the emotion data sent from the terminal and evaluates the driver's stress and fatigue level. The input is emotion data, and the output is the analyzed emotion evaluation result.

[0732] The results of the evaluation are used to adjust the operation plan and make necessary changes based on the driver's condition.

[0733] Specific behavior: If the server determines that the driver's stress level is high, it will immediately recalculate the route and increase the number of rest points.

[0734] User action steps

[0735] Step 1: Verify the route

[0736] The user checks the route displayed on the terminal and starts driving according to the instructions. The input is the operation plan displayed on the terminal, and the output is the specific actions to be taken.

[0737] Efficient operation is possible by having the vehicle proceed from the starting point based on the displayed route.

[0738] Specific operation: The user follows the instructions on the device and proceeds along the displayed route.

[0739] Step 2: Provide feedback

[0740] The user inputs information about traffic conditions, weather changes, and emotional state into the terminal. The input is the feedback information that the driver inputs into the terminal, and the output is the process of sending that information to the server.

[0741] For example, if you enter "the road is congested," that information is immediately sent to the server and the route is recalculated.

[0742] Specific operation: When the user types "The weather has suddenly changed and it has started to snow" into the device, that information is sent to the server, which then recalculates the route.

[0743] Example prompts for generative AI models

[0744] "How can I get real-time traffic information for a specific location using the Google Maps API?"

[0745] "Please provide an example of an algorithm for recognizing emotions from a driver's facial expressions and tone of voice."

[0746] (Application example 2)

[0747] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0748] In modern logistics systems, it is important to generate optimal logistics routes by responding to real-time changes in road conditions and weather information. Furthermore, to reduce driver workload and achieve efficient delivery, it is necessary to recognize the driver's emotional state and reflect it in the operation plan. However, conventional systems that process these factors in an integrated manner lack real-time information updates and driver emotional analysis, making it difficult to provide a safe and efficient working environment.

[0749] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0750] In this invention, the server includes a data collection means for collecting road condition and weather information in real time, a data analysis means for analyzing road congestion, weather fluctuations, and driver fatigue levels based on the collected data, a route optimization means for generating an optimal logistics route based on the analysis results and creating an operation plan that takes driver workload into consideration, an emotion recognition means for recognizing and analyzing the emotional state of the driver, and an operation plan adjustment means for adjusting the operation plan based on the emotional state of the driver. This enables real-time information updates and driver emotion analysis, making it possible to realize a safe and efficient logistics system.

[0751] "Data collection means" means a device or system that collects road condition and weather information in real time.

[0752] "Data analysis means" refers to a device or system that analyzes road congestion, weather fluctuations, and driver fatigue based on collected data.

[0753] "Route optimization means" refers to a device or system that generates optimal logistics routes based on the results of data analysis and creates operation plans that take driver workload into consideration.

[0754] "Emotion recognition means" refers to a device or system for recognizing and analyzing the emotional state of a driver.

[0755] "Operation plan adjustment means" refers to a device or system that adjusts the operation plan based on the emotional state of the driver.

[0756] "Display means" refers to a device or system that displays the optimum route and operation plan to the driver and notifies them of traffic information and weather forecast updates in real time.

[0757] "Feedback collection means" refers to a device or system that collects feedback from drivers and transmits it to a server to improve the route.

[0758] This invention is a new logistics system that utilizes generative AI, which collects road and weather information in real time and proposes optimal logistics routes based on that information. Furthermore, to reduce the burden on drivers and achieve efficient logistics, it combines an emotion engine that recognizes the user's emotions. To realize this system, it has the following functions:

[0759] Server Operation

[0760] Data collection

[0761] The server obtains real-time road and weather information from external APIs. This allows it to collect current traffic volume, accident information, weather conditions, and more. Specifically, the server uses Google Maps API to collect traffic congestion information and OpenWeatherMap API to obtain weather data. It also obtains cargo information, driver work status, and vehicle status from the logistics management system.

[0762] Data analysis

[0763] The collected information is analyzed on a server. AI algorithms are used to evaluate road congestion, weather fluctuations, and driver fatigue. Delivery priorities are also determined taking into account product characteristics (weight, volume, priority). For example, an AI model analyzes road congestion and weather fluctuations and adjusts delivery schedules to deliver products with high delivery priorities first.

[0764] Route Optimization

[0765] Based on the analysis results, the server generates an optimal logistics route. The generated route takes into account the driver's workload and includes rest stops and a schedule that prevents continuous driving of less than five hours. For example, it suggests a detour route that uses the highway to avoid traffic jams.

[0766] Device behavior

[0767] Displaying instructions

[0768] The device displays the optimized route and operation plan sent from the server to the driver. It also notifies the driver of real-time traffic and weather updates. For example, it displays specific instructions such as "The next right turn is 2 kilometers away. At your current speed, you can reach it within 10 minutes."

[0769] Collecting feedback

[0770] The device collects feedback from the driver. When the driver inputs changes in road conditions or weather into the device, the information is immediately sent to the server. For example, if the driver inputs "Delay due to road construction," the information is sent to the server.

[0771] Emotion Engine Operation

[0772] Emotion recognition

[0773] The device is equipped with an emotion engine that recognizes the driver's emotional state in real time. Using cameras, microphones, and various sensors, it reads emotions from the driver's facial expressions and tone of voice. For example, if the driver is feeling stressed, that information will be detected by the device.

[0774] Emotion Analysis

[0775] The server analyzes the emotion data sent from the device and evaluates the driver's stress and fatigue level. The evaluation results are used to adjust the operation plan. For example, if the emotion analysis shows that the driver is feeling highly stressed, the planned route can be changed and rest stops added.

[0776] Hardware and software used

[0777] The main hardware used is a smartphone, smart glasses, a head-mounted display, and a server, while the software used includes Google Maps API, OpenWeatherMap API, Emotion API, TensorFlow, React Native, and Node.js.

[0778] Examples and prompts

[0779] For example, for emotion recognition, the following prompt sentences are used:

[0780] Example prompt sentence:

[0781] "If a driver is feeling stressed, identify that emotion in real time and adjust your operation plan."

[0782] Example of a prompt for real-time route generation:

[0783] "Generate the most efficient and safe route, taking into account your current location and weather information."

[0784] This system reduces the burden on drivers while providing optimal logistics routes in real time, achieving efficient logistics and cost reductions. In addition, the emotion engine recognizes the emotional state of drivers and uses this information to adjust operation plans, providing a safer and more comfortable working environment.

[0785] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0786] Step 1:

[0787] The server collects road conditions and weather information in real time. It uses the Google Maps API to obtain traffic information (e.g., traffic congestion information) and the OpenWeatherMap API to obtain the latest weather data. Specifically, it sends the current coordinates as input to each API and stores the obtained traffic and weather information in an internal database.

[0788] Input: Current coordinates

[0789] Output: Latest traffic information, weather data

[0790] Step 2:

[0791] The server uses AI algorithms to analyze road congestion and weather fluctuations based on the collected traffic information and weather data. It also analyzes driver fatigue levels and vehicle conditions obtained from the logistics management system. This allows the AI ​​model to combine each piece of data to create basic data for generating optimal logistics routes.

[0792] Input: Traffic information, weather data, driver fatigue level, vehicle condition

[0793] Output: Analysis results (road congestion, weather fluctuations, driver status)

[0794] Step 3:

[0795] The server generates optimal logistics routes based on the analysis results and creates operation plans that take driver workloads into account. Specifically, the schedule is set taking into account driver rest stops and continuous driving time. The generated operation plan also includes the priority of the next delivery and detour routes.

[0796] Input: Analysis results

[0797] Output: Optimal logistics routes and operation plans

[0798] Step 4:

[0799] The device displays the optimal route and operation plan sent from the server to the driver. The screen also displays the distance to the next right turn and the arrival time based on speed. It also has a function to notify the driver of traffic information and weather forecast updates in real time.

[0800] Input: Optimal logistics route, operation plan

[0801] Output: Display to driver (route guidance, traffic information, weather forecast)

[0802] Step 5:

[0803] The device collects feedback from the driver. When the driver inputs information about road conditions and weather changes into the device, the data is immediately sent to the server. This feedback information is used by the server to improve the route.

[0804] Input: Driver feedback (road conditions, weather changes)

[0805] Output: Send to server, collect feedback information

[0806] Step 6:

[0807] The device's emotion recognition engine recognizes the driver's emotional state in real time. Specifically, it uses the camera and microphone in the smart glasses or head-mounted display to analyze the driver's facial expressions and tone of voice to determine their stress and fatigue levels. This information is recorded on the device as emotion recognition data and sent to a server.

[0808] Input: Driver's facial expression data, voice tone

[0809] Output: Emotion recognition data (stress, fatigue level)

[0810] Step 7:

[0811] The server analyzes the emotion recognition data sent from the device and evaluates the driver's stress and fatigue level. Based on this evaluation result, the operation plan is adjusted. For example, if the driver is feeling very stressed, the planned route may be changed and adjustments made such as increasing rest stops.

[0812] Input: Emotion recognition data

[0813] Output: Adjusted trip plan

[0814] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0815] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0816] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0817] [Third embodiment]

[0818] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0819] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0820] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0821] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0822] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0823] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0824] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0825] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0826] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0827] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0828] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0829] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0830] This invention is a new logistics system that utilizes generative AI. This system collects road and weather information in real time and can quickly propose optimal logistics routes. It also provides plans to minimize driver workload, aiming to reduce transportation costs and improve driver working conditions.

[0831] Server Operation

[0832] Data collection

[0833] The server obtains real-time road and weather information from external APIs. This allows it to collect current traffic volume, accident information, weather conditions, etc. It also obtains cargo information, driver work status, and vehicle status from the logistics management system. For example, the server obtains road information from the Google Maps API and weather information from the OpenWeatherMap API, while simultaneously understanding the driver's work status from the traffic management system.

[0834] Data analysis

[0835] The collected information is analyzed on a server. An AI algorithm is used to analyze the collected data and evaluate road congestion, weather fluctuations, and driver fatigue. Delivery priorities are also determined taking into account product characteristics (weight, volume, priority, etc.). For example, an AI model analyzes road congestion and weather fluctuations to generate a delivery plan that prioritizes the delivery of high-priority products.

[0836] Route Optimization

[0837] Based on the analysis results, the server generates an optimal logistics route. The generated route takes into account the driver's workload, avoids long periods of continuous driving, and includes regular rest stops. For example, the server may suggest detour routes to avoid traffic jams and create a plan for efficient delivery by using expressways.

[0838] Device behavior

[0839] Displaying instructions

[0840] The device displays the optimized route and operation plan sent from the server to the driver. It also notifies the driver of traffic information and weather forecast updates in real time. For example, the device displays specific instructions such as "The next right turn is 2 kilometers away. At your current speed, you can reach it within 10 minutes."

[0841] Collecting feedback

[0842] The device collects feedback from the driver. When the driver inputs road conditions or changes in weather into the device, that information is immediately sent to the server, and the route is improved. For example, if the driver inputs "delays due to road construction" into the device, that information is sent to the server and reanalyzed.

[0843] User Actions

[0844] Check the route

[0845] The user (driver) checks the route displayed on the device and starts driving according to the instructions. For example, the user follows the instructions on the device to turn right and proceed to the next destination.

[0846] Providing Feedback

[0847] Users input changes in traffic conditions and weather into their devices. If there is new information, it is immediately entered into the device and sent to the server, which then revises the route. For example, "the user can input that the road is congested into the device, and that data is sent to the server, which then generates a new optimal route."

[0848] This system will reduce the burden on drivers while improving logistics efficiency and reducing costs.

[0849] The processing flow will be explained below.

[0850] Step 1:

[0851] The server collects road conditions and weather information in real time.

[0852] The server periodically obtains current traffic and weather information using external APIs. Specifically, the server uses the Google Maps API to collect road congestion and accident information, and the OpenWeatherMap API to obtain current weather information. The server also obtains cargo information and driver work status from the logistics management system (WMS) and the transportation management system (TMS).

[0853] Step 2:

[0854] The server performs analysis based on the collected data.

[0855] The data collected on the server is analyzed using an AI model. The analysis evaluates road congestion, weather fluctuations, and driver fatigue. Product characteristics (weight, volume, priority) are also taken into account to determine delivery priorities. For example, the server inputs the collected data into the AI ​​to determine whether a detour route is necessary if a particular road is congested.

[0856] Step 3:

[0857] The server generates optimal routes and operation plans.

[0858] Based on the results of the data analysis, the server generates an optimal logistics route that takes into account the driver's workload. The generated route includes rest stops for the driver and a schedule to prevent continuous driving of less than five hours. For example, the server suggests using highways or taking shorter routes to avoid traffic jams.

[0859] Step 4:

[0860] The terminal displays the optimal route and operation plan received from the server to the driver.

[0861] The device receives data from the server and displays the optimal route and trip plan to the driver. It also provides real-time updates on traffic and weather forecasts. For example, the device might display specific instructions such as, "The next right turn is 2 kilometers away. At your current speed, you can reach it within 10 minutes."

[0862] Step 5:

[0863] The user checks the displayed route and starts driving.

[0864] The user (driver) checks the route displayed on the terminal and starts driving according to the instructions, for example, driving the car towards the destination according to the clearly indicated route.

[0865] Step 6:

[0866] The device collects feedback from the driver.

[0867] The device accepts feedback from the driver. When the driver inputs new information into the device, such as traffic conditions or weather changes, the information is immediately sent to the server. For example, if the driver inputs "Delay due to road construction," the information is sent to the server.

[0868] Step 7:

[0869] The server will re-analyze the route based on the new data.

[0870] The server re-analyzes the data based on the feedback sent by the driver. This re-analysis reflects the latest traffic and weather information and re-generates an optimized route. For example, if road construction is reported, the server will suggest a new detour route.

[0871] This series of processing steps provides drivers with logistics routes optimized in real time, reducing the burden on drivers and achieving efficient logistics.

[0872] Example 1

[0873] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0874] Current logistics systems have difficulty effectively utilizing real-time information on road conditions and weather to generate optimal delivery routes, resulting in increased workloads for workers and rising transportation costs. Another issue is the insufficient means for incorporating feedback from workers into the entire system, making it difficult to quickly improve routes.

[0875] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0876] In this invention, the server includes a data acquisition means for collecting road condition and weather information in real time via a digital data interface, a data analysis means for analyzing road congestion, weather fluctuations, and worker fatigue levels based on the collected data, a route optimization means for generating optimal delivery routes based on the results of the data analysis means and creating operation plans that take worker burdens into consideration, and a feedback collection means for dynamically receiving input from workers and improving routes across the entire system. This makes it possible to generate fast and efficient delivery routes based on environmental information that changes in real time, reduce worker burdens, and reduce transportation costs.

[0877] A "digital data interface" is a communication means for collecting information from various data sources in real time.

[0878] "Data acquisition means" is a function that acquires necessary information in real time from external APIs and internal systems.

[0879] "Data analysis means" refers to AI algorithms and related tools for analyzing collected data and predicting and assessing changes in the situation.

[0880] A "route optimization method" is a means for generating optimal logistics routes based on analysis results and creating operation plans that take into account the burden on workers.

[0881] The "feedback collection means" is a function that sends real-time feedback from workers to the server and uses it to reevaluate the system and improve routes.

[0882] "Display means" refers to a device that visually presents optimal routes and operation plans, as well as real-time traffic and weather information to workers.

[0883] "Communication means" refers to a means for transmitting and receiving data packets between the server and the worker's terminal.

[0884] This invention is a system that utilizes generative AI models to improve logistics efficiency and reduce the burden on workers. By linking the elements of the server, terminals, and users, this system generates optimal delivery routes that take into account changing road conditions and weather information in real time, and can adjust operation plans to reflect worker feedback.

[0885] Hardware and software used

[0886] server

[0887] The server collects various data via a digital data interface, analyzes the data using a generative AI model, and performs route optimization. Specifically, it uses the following APIs and software:

[0888] Google Maps API: Obtains real-time road condition data such as traffic volume and accident information.

[0889] OpenWeatherMap API: Get real-time weather information.

[0890] TensorFlow: A library for running AI algorithms required for data analysis.

[0891] Terminal

[0892] The device visually displays the optimal route and operation plan sent from the server, and also provides real-time updates on traffic and weather forecasts and collects feedback from workers.

[0893] GPS navigation software: used to provide route guidance.

[0894] Real-time notification system: Notify workers of traffic and weather updates.

[0895] User (worker)

[0896] The user (worker) drives the vehicle according to the instructions displayed on the terminal and inputs feedback on road conditions and weather changes into the terminal.

[0897] Program processing

[0898] The server collects real-time road and weather information via the Google Maps API and OpenWeatherMap API. It then formats the data and converts it into a consistent format. The collected data is analyzed using a generative AI model (e.g., TensorFlow). Based on the analysis results, the server generates optimal delivery routes and creates operation plans that take into account worker workloads.

[0899] The plan is sent from the server to the device, which displays it visually to the worker. Additionally, traffic and weather updates are communicated to the worker in real time through visual and audio notifications. The worker enters feedback on road conditions and weather changes into the device, which sends this information to the server. The server reanalyzes the route based on the new information and updates the trip plan as needed.

[0900] Specific examples

[0901] For example, the server retrieves traffic information using the Google Maps API and rainfall information using the OpenWeatherMap API. The collected data is analyzed using the TensorFlow library to generate a delivery plan that delivers high-priority items (e.g., medical supplies) first. The terminal displays instructions such as, "The next right turn is 2 kilometers away. At your current speed, you can reach it within 10 minutes."

[0902] Prompt Sentence Examples

[0903] You are a logistics manager. You want to use the latest technology to reduce the workload of your workers and ensure efficient deliveries. There is currently congestion on the roads, but no rain. Please suggest the following optimal route to prioritize the urgent delivery of medical supplies:

[0904] This system will enable the generation of fast and efficient delivery routes based on real-time changing environmental information, reducing the burden on workers and cutting transportation costs.

[0905] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0906] Step 1:

[0907] The server collects road and weather information in real time via a digital data interface. Specifically, it uses the Google Maps API to obtain traffic and accident information, and the OpenWeatherMap API to obtain weather information. The server obtains data by sending parameters such as an API key and location information. The input is a request to the API, and the output is the latest road and weather information.

[0908] Step 2:

[0909] The server converts the collected data into a consistent format. For example, data acquired in JSON format can be converted to CSV format and invalid data can be removed. This preprocessing step also performs missing value imputation and noise removal. The input is the collected raw data, and the output is the formatted data.

[0910] Step 3:

[0911] The server analyzes the preprocessed data using an AI algorithm (e.g., TensorFlow). Specifically, it uses an LSTM model to predict road congestion and weather fluctuations, and performs analysis that takes driver fatigue into account. The input is the formatted data, and the output is the predicted results of road congestion, weather fluctuations, and driver fatigue.

[0912] Step 4:

[0913] The server generates multiple delivery route candidates based on the analysis results. For example, it simulates multiple routes using discrete event simulation. At this stage, the server takes product characteristics (weight, volume, priority) into account and generates a route that prioritizes high-priority products. The inputs are the analysis results and product characteristic data, and the output is multiple route candidates.

[0914] Step 5:

[0915] The server evaluates the generated route candidates and selects the most efficient route that places the least strain on the driver. For example, it evaluates each route using a cost function and determines the optimal route based on parameters such as total travel time and number of rest stops. The input is the route candidates, and the output is the optimal route.

[0916] Step 6:

[0917] The server sends the optimal route and operation plan to the terminal. Specifically, it uses an HTTP POST request to send the operation plan to the terminal as a data packet. The optimal route and operation plan are input, and are sent to the terminal as output.

[0918] Step 7:

[0919] The device receives route information and visually displays it to the driver. For example, it uses a mapping app to display the route on a map and provide turn-by-turn instructions. It also notifies the driver of real-time traffic and weather updates. The input is the route information and trip plan sent from the server, and the output is presented to the driver in the form of visuals and notifications.

[0920] Step 8:

[0921] The user (driver) inputs feedback about road conditions and weather changes into the device. Specifically, feedback such as "road construction" is input using voice input or a touch screen. The input can be single or continuous feedback data, and the output is stored in the device and sent to the server.

[0922] Step 9:

[0923] The terminal immediately sends the collected feedback to the server, for example, by sending the feedback data to the " / feedback / update" endpoint via an HTTP POST request. The input is the feedback data from the driver, and the output is sent to the server.

[0924] Step 10:

[0925] The server reanalyzes the route based on the new feedback information and updates it as necessary. Specifically, it analyzes the feedback information and again uses an AI algorithm to recalculate the optimal route. The input is the new feedback information, and the output is an updated optimal route. This new route is again sent to the device and notified to the driver.

[0926] (Application example 1)

[0927] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0928] Current logistics systems often do not utilize real-time information on road conditions and weather, resulting in frequent delivery delays due to traffic congestion and bad weather. Furthermore, operation plans are often made without taking driver fatigue into account, leaving drivers with no improvement in their working conditions. Furthermore, there is a lack of systems that can immediately respond to changes in road conditions and weather that differ from the current situation, making efficient delivery difficult.

[0929] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0930] In this invention, the server includes a data collection means for collecting road conditions and weather information in real time, a data analysis means for analyzing road congestion, weather fluctuations, and driver fatigue based on the collected data, and a route optimization means for generating an optimal logistics route based on the analysis results and creating an operation plan that takes driver workload into consideration. This makes it possible to propose efficient and rapid logistics routes and reduce driver workload.

[0931] "Data collection means for collecting road conditions and weather information in real time" refers to a device or program for continuously obtaining current road conditions and weather information from an external API, etc.

[0932] "Data analysis means for analyzing road congestion, weather changes, and driver fatigue level based on collected data" refers to a device or program for analyzing collected information and evaluating the current state of road congestion, weather changes, and driver fatigue level.

[0933] "Route optimization means for generating optimal logistics routes based on analysis results and creating operation plans that take driver workload into consideration" refers to a device or program that generates routes and operation plans to optimize delivery efficiency and the driver's working environment based on analyzed data.

[0934] The "feedback receiving means for receiving input of information while driving" is a device or program for collecting feedback information (such as changes in road conditions and weather) from the driver.

[0935] The "re-route generation means for reanalyzing and re-proposing an optimal route based on input information" is a device or program for calculating and proposing a new optimal route based on feedback information from the driver.

[0936] "Display means for displaying the optimal route and operation plan to the driver and notifying him of updates to traffic information and weather forecasts in real time" refers to a device or program that displays the generated route and operation plan, as well as the latest traffic and weather information, in an easy-to-understand manner to the driver.

[0937] The "feedback collection means for collecting feedback from the driver and transmitting it to a server to improve the route" is a device or program for collecting feedback information from the driver and transmitting the information to a server to generate a new optimal route.

[0938] Overall system overview

[0939] This invention is a system that uses a generative AI model to propose optimal logistics routes in real time. The system consists of a server, terminals, and users, all of which work in conjunction with each other. The detailed roles and operations of each component are explained below.

[0940] Server Operation

[0941] Data collection

[0942] The server collects data using the following external APIs:

[0943] Road information: Real-time road conditions are collected from external map service APIs (general name: map information provision APIs).

[0944] Weather information: Obtain real-time weather information from a weather information API.

[0945] Operation management system: Obtains cargo information, driver work status, and vehicle status from logistics management.

[0946] Examples:

[0947] Road information is obtained from the map information API, and weather information is obtained from the weather information API.

[0948] The collected information is stored in the server's disk storage.

[0949] Data analysis

[0950] A generative AI model is used to analyze the collected data.

[0951] Congestion analysis: Evaluate the degree of congestion on roads.

[0952] Weather Variability Analysis: Assess current and forecasted weather.

[0953] Fatigue analysis: Evaluate the driver's fatigue level based on their work status.

[0954] Examples:

[0955] The driver's fatigue level is evaluated from the driving status log.

[0956] A generative AI model analyzes data on congestion and weather fluctuations to generate a delivery plan that prioritizes high-priority items.

[0957] Route Optimization

[0958] Route optimization tools are used to generate optimal routes and operation plans based on the collected and analyzed data.

[0959] Route generation: A generative AI model suggests the optimal route.

[0960] Rest Point Suggestion: Suggest appropriate rest points to avoid long driving times.

[0961] Examples:

[0962] The server suggests the optimal route while taking into account the driver's level of fatigue.

[0963] The generated route includes plans to use expressways.

[0964] Device behavior

[0965] Displaying instructions

[0966] The terminal displays the optimal route and operation plan sent from the server to the driver.

[0967] Real-time notifications: Traffic and weather updates and notifications in real time.

[0968] Examples:

[0969] Specific instructions such as "The next right turn is 2 kilometers away. At your current speed, you can get there in 10 minutes" are displayed on the device.

[0970] Collecting feedback

[0971] Feedback from drivers is collected and sent to the server to improve routes.

[0972] Information input: The driver inputs changes in road conditions and weather.

[0973] Server submission: The input information is immediately sent to the server for re-analysis.

[0974] Examples:

[0975] If a driver feels they are stuck in traffic, they can enter "Stuck in Traffic."

[0976] Based on the input information, a new optimal route is generated by the server and sent back to the terminal.

[0977] User Actions

[0978] Check the route

[0979] The user (driver) checks the route displayed on the terminal and starts driving according to the instructions.

[0980] Examples:

[0981] "The user follows the instructions on the device to turn right and proceed to their next destination."

[0982] Providing Feedback

[0983] Users can input changes in traffic conditions and weather into their devices and have the route revised by sending the information to the server.

[0984] Examples:

[0985] "The user inputs into the device that the road is congested, and the data is sent to the server."

[0986] Prompt Sentence Examples

[0987] Below are some example prompts to be input to the AI ​​model.

[0988] The user's current location is [latitude, longitude] and their destination is [latitude, longitude]. The following road information was obtained from the map information API: ... The following weather information was obtained from the weather information API: ... Please suggest a route for a high-priority delivery based on the current road conditions and weather.

[0989] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0990] Step 1:

[0991] The server collects real-time road conditions and weather information from external APIs. Specifically, it obtains road congestion data from a map information API and weather data from a weather information API. The obtained data is stored in a database. The input is traffic and weather information from the API, and the output is the stored data.

[0992] Step 2:

[0993] The server uses a generative AI model to analyze the collected data. First, it evaluates road congestion, then analyzes weather fluctuations, and finally evaluates the driver's work status and fatigue level. The input is the stored traffic and weather information, and the output is the congestion level of each road, weather fluctuations, and driver fatigue level assessment results.

[0994] Step 3:

[0995] The server uses a generative AI model based on the analysis results to generate the optimal logistics route. It also creates an operation plan that takes into account the driver's workload. This involves generating a route that takes into account the priority of deliveries and includes necessary rest stops. The inputs are the analysis results and delivery information, and the output is the optimal route and operation plan.

[0996] Step 4:

[0997] The terminal receives the optimal route and operation plan sent from the server and displays them to the driver. It also notifies the driver of real-time updates on traffic information and weather forecasts. Specifically, it displays instructions such as "2 kilometers to the next right turn." The input is the optimal route and operation plan, and the output is the notification to the driver.

[0998] Step 5:

[0999] When the driver inputs changes in road conditions or weather into the terminal while driving, the terminal sends that feedback information to the server. The input is feedback information from the driver, and the output is the transmission of that information to the server.

[1000] Step 6:

[1001] Based on the received feedback, the server reanalyzes the route using a generative AI model and proposes a new optimal route. This information is sent to the device in real time. The input is the feedback, and the output is the reanalyzed new optimal route.

[1002] Step 7:

[1003] The terminal receives updated route information from the server and displays the new route to the driver. The input is the new optimal route information, and the output is the new notification content to the driver.

[1004] Step 8:

[1005] The user (driver) then starts driving again according to the new route and inputs further feedback information into the terminal as necessary. This allows the driver and the system to work together to achieve efficient logistics. The input is the new route information and the driver's feedback, and the output is route optimization by the system and improvements to the driver's driving.

[1006] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1007] This invention is a new logistics system that utilizes generative AI to collect road and weather information in real time and propose optimal logistics routes based on that information. Furthermore, to reduce the driver's workload and achieve efficient logistics, it incorporates an emotion engine that recognizes the user's emotions. This system analyzes the driver's emotional state and reflects this in the operation plan, resulting in more precise route optimization.

[1008] Server Operation

[1009] Data collection

[1010] The server obtains real-time road and weather information from external APIs. This allows it to collect current traffic volume, accident information, weather conditions, and more. For example, the server uses the Google Maps API to collect traffic congestion information and the OpenWeatherMap API to obtain weather data. It also obtains cargo information, driver work status, and vehicle status from the logistics management system.

[1011] Data analysis

[1012] The collected information is analyzed on a server. AI algorithms are used to evaluate road congestion, weather fluctuations, and driver fatigue. Delivery priorities are also determined taking into account product characteristics (weight, volume, priority). For example, an AI model analyzes road congestion and weather fluctuations and adjusts delivery schedules to deliver products with high delivery priorities first.

[1013] Route Optimization

[1014] Based on the analysis results, the server generates an optimal logistics route. The generated route takes into account the driver's workload and includes rest stops and a schedule that prevents continuous driving of less than five hours. For example, it suggests a detour route that uses the highway to avoid traffic jams.

[1015] Device behavior

[1016] Displaying instructions

[1017] The device displays the optimized route and operation plan sent from the server to the driver. It also notifies the driver of real-time traffic and weather updates. For example, it displays specific instructions such as "The next right turn is 2 kilometers away. At your current speed, you can reach it within 10 minutes."

[1018] Collecting feedback

[1019] The device collects feedback from the driver. When the driver inputs changes in road conditions or weather into the device, the information is immediately sent to the server. For example, if the driver inputs "Delay due to road construction," the information is sent to the server.

[1020] Emotion Engine Operation

[1021] Emotion recognition

[1022] The device is equipped with an emotion engine that recognizes the driver's emotional state in real time. Using cameras, microphones, and various sensors, it reads emotions from the driver's facial expressions and tone of voice. For example, if the driver is feeling stressed, that information will be detected by the device.

[1023] Emotion Analysis

[1024] The server analyzes the emotion data sent from the device and evaluates the driver's stress and fatigue level. The evaluation results are used to adjust the operation plan. For example, if the emotion analysis shows that the driver is feeling highly stressed, the planned route can be changed and rest stops added.

[1025] User Actions

[1026] Check the route

[1027] The user (driver) checks the route displayed on the terminal and starts driving according to the instructions. For example, the user drives the car from the starting point based on the displayed route.

[1028] Providing Feedback

[1029] Users input information about traffic conditions, weather changes, and emotional states into the device. If there is new information, they input it into the device and the data is sent to the server. For example, if you input "the roads are congested" into the device, the information is immediately sent to the server.

[1030] This system reduces the workload of drivers while providing optimal logistics routes in real time, achieving efficient logistics and cost reduction. In addition, the emotion engine recognizes the driver's emotional state and helps adjust operation plans, providing a safer and more comfortable working environment.

[1031] The processing flow will be explained below.

[1032] Step 1:

[1033] The server collects road conditions and weather information in real time.

[1034] The server periodically obtains current traffic and weather information using external APIs. For example, the server uses the Google Maps API to collect traffic and accident information, and the OpenWeatherMap API to obtain current weather information. The server also obtains cargo information and driver work status from the Warehouse Management System (WMS) and the Transportation Management System (TMS).

[1035] Step 2:

[1036] The server performs analysis based on the collected data.

[1037] The data collected on the server is analyzed using an AI model. The analysis evaluates road congestion, weather fluctuations, and driver fatigue. Delivery priorities are also determined, taking into account product characteristics (weight, volume, priority). For example, the AI ​​model analyzes road congestion and weather fluctuations and determines if detours are necessary.

[1038] Step 3:

[1039] The server generates optimal routes and operation plans.

[1040] Based on the results of the data analysis, the server generates an optimal logistics route that takes into account the driver's workload. This generated route includes a schedule that limits rest stops and continuous driving time. For example, it suggests routes that use highways to avoid traffic jams and plans to insert a 20-minute break after three hours of driving.

[1041] Step 4:

[1042] The terminal displays the optimal route and operation plan received from the server to the driver.

[1043] The device receives data from the server and displays the optimal route and trip plan to the driver. It also provides real-time updates on traffic and weather forecasts. For example, the device might display, "The next right turn is 2 kilometers away, and you can reach it in 10 minutes at your current speed."

[1044] Step 5:

[1045] The user checks the displayed route and starts driving.

[1046] The user (driver) checks the route displayed on the terminal and starts driving according to the instructions. Specifically, the driver drives the vehicle from the departure point to the destination based on the displayed route.

[1047] Step 6:

[1048] The device recognizes the driver's emotions in real time.

[1049] The device uses cameras, microphones, and various sensors to read the driver's emotions from their facial expressions and tone of voice. The emotion engine determines the driver's stress level and fatigue. For example, the device can use the driver's facial recognition camera to detect when the driver is tired.

[1050] Step 7:

[1051] The server performs analysis based on the emotion data.

[1052] The server receives and analyzes the emotion data sent from the device. The server evaluates the driver's stress level and fatigue level and adjusts the trip plan based on the results. For example, if the driver is in a high-stress state, the server will modify the existing trip plan to include additional breaks.

[1053] Step 8:

[1054] The device collects feedback from the driver.

[1055] The device accepts feedback from the driver. When the driver inputs information about road conditions, weather changes, or a new emotional state, that information is immediately sent to the server. For example, if the driver inputs "Delay due to road construction" into the device, that information is sent to the server and reanalyzed.

[1056] Step 9:

[1057] The server will re-analyze the route based on the new data.

[1058] The server then re-analyzes the data based on the feedback and emotion data sent by the driver. This re-analysis reflects the latest traffic and weather information, as well as the driver's emotional state, and re-generates an optimized route. For example, if road construction is reported, the server will suggest a new detour route and set additional rest stops according to the driver's stress level.

[1059] This series of processing steps provides drivers with optimized logistics routes in real time, reducing their workload and achieving efficient logistics. Furthermore, the emotion engine monitors the driver's emotional state in real time and reflects this in the operation plan, ensuring driver safety and effective delivery.

[1060] Example 2

[1061] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1062] Conventional logistics systems often generate inefficient operation plans because they are unable to properly consider changes in road conditions and weather information, as well as driver fatigue levels. It is also difficult to grasp the emotional state of the driver, making it difficult to achieve safe operation and reduce driver burden. There is a need to solve these problems and provide efficient and safe operation plans.

[1063] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1064] In this invention, the server includes a data collection means for collecting road condition and weather information in real time, a data analysis means for analyzing road congestion, weather fluctuations, and driver fatigue based on the collected data, a route optimization means for generating an optimal logistics route based on the analysis results and creating an operation plan that takes driver workload into consideration, and an emotion analysis means for analyzing the driver's emotional state and reflecting it in the operation plan. This makes it possible to generate an efficient and safe operation plan that comprehensively takes into consideration road conditions, weather, and the driver's condition and emotion.

[1065] "Data collection means" means devices and methods for obtaining real-time road condition and weather information from external sources.

[1066] "Data analysis means" refers to a method and device that uses AI algorithms to analyze road congestion, weather fluctuations, and driver fatigue levels based on collected road and weather information.

[1067] The "route optimization means" is a method and device for generating an optimal logistics route and creating an operation plan while taking into consideration the driver's workload, based on the analysis results obtained by the data analysis means.

[1068] The "emotion analysis means" is a method and device for monitoring and analyzing the driver's emotional state and reflecting it in the operation plan.

[1069] "Display means" refers to a device and method that displays optimal routes and operation plans to drivers and notifies them of traffic information and weather forecast updates in real time.

[1070] "Feedback collection means" refers to devices and methods for collecting driver feedback and transmitting it to a server for route improvement.

[1071] This system collects road and weather information in real time and proposes optimal logistics routes based on that information. Furthermore, it analyzes the driver's emotional state and reflects it in the operation plan, achieving more precise route optimization.

[1072] Server Operation

[1073] Data collection methods

[1074] The server uses the Google Maps API to obtain real-time road information, which allows it to collect traffic volume, accident information, and congestion information. It also uses the OpenWeatherMap API to obtain weather data and obtain current weather and forecasts. Furthermore, it collects vehicle load information, driver work status, and vehicle condition information from the logistics management system.

[1075] Data Analysis Methods

[1076] The server uses collected road and weather information to perform data analysis using AI algorithms. Specifically, it evaluates road congestion, accident rates, and weather fluctuations. It determines delivery priorities by taking into account the driver's work schedule, vehicle condition, and cargo information.

[1077] Route optimization methods

[1078] The server then generates optimal logistics routes based on the results of the data analysis. These routes take into account the driver's workload and include rest stops and a schedule that limits continuous driving to five hours. For example, it suggests detour routes, including using highways to avoid traffic jams.

[1079] Emotion analysis means

[1080] The server analyzes the driver's emotional data, including facial expressions and tone of voice, sent from the device. This data is used to evaluate the driver's stress and fatigue level, and the results are reflected in the operation plan. Based on the results of the driver's emotional analysis, the server can increase rest stops or readjust the route as necessary.

[1081] Device behavior

[1082] Display means

[1083] The device displays the optimized route and operation plan sent from the server to the driver. It also notifies the driver of real-time traffic and weather updates while driving. For example, it displays specific instructions such as, "The next right turn is 2 kilometers away. At your current speed, you can reach it within 10 minutes."

[1084] Feedback collection methods

[1085] The device collects feedback from the driver: if the driver types in "delay due to roadworks," the information is immediately sent to the server, which recalculates the route.

[1086] User Actions

[1087] Check the route

[1088] The user checks the route displayed on the terminal and starts driving according to the instructions, for example, by moving the car from the starting point based on the displayed route.

[1089] Providing Feedback

[1090] Users input information about traffic conditions, weather changes, and their emotional state into the device. For example, if they input "the roads are congested," that information is immediately sent to the server, and the route is recalculated.

[1091] Specific examples

[1092] 1. The server uses the Google Maps API to obtain real-time traffic information.

[1093] 2. The server analyzes the collected information and the driver's work status to generate a driving plan including the optimal route and rest points.

[1094] 3. The device will display instructions to the driver saying, "The next right turn is 2 kilometers away. At your current speed, you can get there in 10 minutes."

[1095] 4. When a driver types "the road is congested" into the device, that information is immediately sent to the server and the route is recalculated.

[1096] Example prompts for generative AI models

[1097] "How can I get real-time traffic information for a specific location using the Google Maps API?"

[1098] "Please provide an example of an algorithm for recognizing emotions from a driver's facial expressions and tone of voice."

[1099] This system reduces the workload of drivers, enabling efficient logistics and cost reduction. It also uses an emotion engine to understand the driver's emotional state and use this information to adjust operation plans, providing a safer and more comfortable working environment.

[1100] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1101] Server Operation Steps

[1102] Step 1: Data collection

[1103] The server obtains real-time road information (traffic volume, accident information, congestion information) through the Google Maps API. The input is the coordinate information of a specific location, and the output is road condition data for the corresponding location. This allows the current traffic situation to be understood.

[1104] The server uses the OpenWeatherMap API to obtain weather data (current weather and forecast). The input is the coordinate information of a location, and the output is the weather information for that location. This allows us to determine the weather conditions that will affect logistics routes.

[1105] Furthermore, the server obtains information on the vehicle's cargo, the driver's work status, the vehicle's condition, etc. from the logistics management system. The input is various data related to the operation plan, and the output is a data set that aggregates this information.

[1106] Specific operation: The server sends a request to the Google Maps API every minute, collects road information, stores it in a database, and updates weather data every hour using the OpenWeatherMap API.

[1107] Step 2: Data analysis

[1108] The server analyzes the collected road and weather information using an AI algorithm. The input is the dataset collected in step 1, and the output is the analysis results regarding road congestion, accident rates, and weather fluctuations. This allows for the evaluation necessary to select logistics routes.

[1109] The server determines delivery priorities based on the driver's work schedule, vehicle status, and cargo information. The inputs are driver work schedule data, vehicle status data, and product data, and the output is a list of products with high delivery priority.

[1110] How it works: AI algorithms analyze data and calculate the optimal route based on current traffic and weather conditions, then select items with high delivery priority and plan the route accordingly.

[1111] Step 3: Route optimization

[1112] Based on the analysis results, the server generates the optimal logistics route taking into account the driver's fatigue level and stress. The input is the analysis results obtained in step 2 and the product list, and the output is the optimized logistics route.

[1113] The route will include rest stops and a schedule that limits continuous operation to five hours, allowing for safe and efficient operation planning.

[1114] Specific operation: Based on the results of the driver's emotion analysis, the server reevaluates the planned route and adds rest stops as necessary. To avoid traffic jams, the server suggests alternative routes, including using the highway.

[1115] Device operation steps

[1116] Step 1: View instructions

[1117] The terminal displays the optimal route and operation plan sent from the server to the driver. The input is the operation plan data from the server, and the output is a display of specific instructions to the driver.

[1118] It provides real-time traffic and weather updates while driving, keeping drivers up to date with the latest information.

[1119] Specific actions: Specific instructions are displayed, such as "The next right turn is 2 kilometers away. At your current speed, you can reach it within 10 minutes." The device also notifies you in real time, "Heavy rain is currently forecast ahead. Please drive safely."

[1120] Step 2: Gather feedback

[1121] The terminal collects feedback from the driver. The input is the feedback information that the driver enters into the terminal, and the output is the process of sending that information to the server.

[1122] For example, if you enter "Delay due to road construction," that information is immediately sent to the server and the route is recalculated.

[1123] Specific operation: When the driver inputs into the device, "The current road conditions are more congested than expected," the device immediately sends that information to the server, which then recalculates the route.

[1124] Emotion Engine Operation Steps

[1125] Step 1: Recognize emotions

[1126] The device uses a camera, microphone, and various sensors to recognize the driver's emotional state in real time. The input is emotional data from the camera and microphone, and the output is the display of the emotional data on the device.

[1127] It assesses the driver's real-time emotional state by reading emotions from their facial expressions and tone of voice.

[1128] How it works: The camera detects signs of stress from the driver's facial expression, and the microphone detects fatigue from the driver's tone of voice.

[1129] Step 2: Sentiment Analysis

[1130] The server analyzes the emotion data sent from the terminal and evaluates the driver's stress and fatigue level. The input is emotion data, and the output is the analyzed emotion evaluation result.

[1131] The results of the evaluation are used to adjust the operation plan and make necessary changes based on the driver's condition.

[1132] Specific behavior: If the server determines that the driver's stress level is high, it will immediately recalculate the route and increase the number of rest points.

[1133] User action steps

[1134] Step 1: Verify the route

[1135] The user checks the route displayed on the terminal and starts driving according to the instructions. The input is the operation plan displayed on the terminal, and the output is the specific actions to be taken.

[1136] Efficient operation is possible by having the vehicle proceed from the starting point based on the displayed route.

[1137] Specific operation: The user follows the instructions on the device and proceeds along the displayed route.

[1138] Step 2: Provide feedback

[1139] The user inputs information about traffic conditions, weather changes, and emotional state into the terminal. The input is the feedback information that the driver inputs into the terminal, and the output is the process of sending that information to the server.

[1140] For example, if you enter "the road is congested," that information is immediately sent to the server and the route is recalculated.

[1141] Specific operation: When the user types "The weather has suddenly changed and it has started to snow" into the device, that information is sent to the server, which then recalculates the route.

[1142] Example prompts for generative AI models

[1143] "How can I get real-time traffic information for a specific location using the Google Maps API?"

[1144] "Please provide an example of an algorithm for recognizing emotions from a driver's facial expressions and tone of voice."

[1145] (Application example 2)

[1146] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1147] In modern logistics systems, it is important to generate optimal logistics routes by responding to real-time changes in road conditions and weather information. Furthermore, to reduce driver workload and achieve efficient delivery, it is necessary to recognize the driver's emotional state and reflect it in the operation plan. However, conventional systems that process these factors in an integrated manner lack real-time information updates and driver emotional analysis, making it difficult to provide a safe and efficient working environment.

[1148] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1149] In this invention, the server includes a data collection means for collecting road condition and weather information in real time, a data analysis means for analyzing road congestion, weather fluctuations, and driver fatigue levels based on the collected data, a route optimization means for generating an optimal logistics route based on the analysis results and creating an operation plan that takes driver workload into consideration, an emotion recognition means for recognizing and analyzing the emotional state of the driver, and an operation plan adjustment means for adjusting the operation plan based on the emotional state of the driver. This enables real-time information updates and driver emotion analysis, making it possible to realize a safe and efficient logistics system.

[1150] "Data collection means" means a device or system that collects road condition and weather information in real time.

[1151] "Data analysis means" refers to a device or system that analyzes road congestion, weather fluctuations, and driver fatigue based on collected data.

[1152] "Route optimization means" refers to a device or system that generates optimal logistics routes based on the results of data analysis and creates operation plans that take driver workload into consideration.

[1153] "Emotion recognition means" refers to a device or system for recognizing and analyzing the emotional state of a driver.

[1154] "Operation plan adjustment means" refers to a device or system that adjusts the operation plan based on the emotional state of the driver.

[1155] "Display means" refers to a device or system that displays the optimum route and operation plan to the driver and notifies them of traffic information and weather forecast updates in real time.

[1156] "Feedback collection means" refers to a device or system that collects feedback from drivers and transmits it to a server to improve the route.

[1157] This invention is a new logistics system that utilizes generative AI, which collects road and weather information in real time and proposes optimal logistics routes based on that information. Furthermore, to reduce the burden on drivers and achieve efficient logistics, it combines an emotion engine that recognizes the user's emotions. To realize this system, it has the following functions:

[1158] Server Operation

[1159] Data collection

[1160] The server obtains real-time road and weather information from external APIs. This allows it to collect current traffic volume, accident information, weather conditions, and more. Specifically, the server uses Google Maps API to collect traffic congestion information and OpenWeatherMap API to obtain weather data. It also obtains cargo information, driver work status, and vehicle status from the logistics management system.

[1161] Data analysis

[1162] The collected information is analyzed on a server. AI algorithms are used to evaluate road congestion, weather fluctuations, and driver fatigue. Delivery priorities are also determined taking into account product characteristics (weight, volume, priority). For example, an AI model analyzes road congestion and weather fluctuations and adjusts delivery schedules to deliver products with high delivery priorities first.

[1163] Route Optimization

[1164] Based on the analysis results, the server generates an optimal logistics route. The generated route takes into account the driver's workload and includes rest stops and a schedule that prevents continuous driving of less than five hours. For example, it suggests a detour route that uses the highway to avoid traffic jams.

[1165] Device behavior

[1166] Displaying instructions

[1167] The device displays the optimized route and operation plan sent from the server to the driver. It also notifies the driver of real-time traffic and weather updates. For example, it displays specific instructions such as "The next right turn is 2 kilometers away. At your current speed, you can reach it within 10 minutes."

[1168] Collecting feedback

[1169] The device collects feedback from the driver. When the driver inputs changes in road conditions or weather into the device, the information is immediately sent to the server. For example, if the driver inputs "Delay due to road construction," the information is sent to the server.

[1170] Emotion Engine Operation

[1171] Emotion recognition

[1172] The device is equipped with an emotion engine that recognizes the driver's emotional state in real time. Using cameras, microphones, and various sensors, it reads emotions from the driver's facial expressions and tone of voice. For example, if the driver is feeling stressed, that information will be detected by the device.

[1173] Emotion Analysis

[1174] The server analyzes the emotion data sent from the device and evaluates the driver's stress and fatigue level. The evaluation results are used to adjust the operation plan. For example, if the emotion analysis shows that the driver is feeling highly stressed, the planned route can be changed and rest stops added.

[1175] Hardware and software used

[1176] The main hardware used is a smartphone, smart glasses, a head-mounted display, and a server, while the software used includes Google Maps API, OpenWeatherMap API, Emotion API, TensorFlow, React Native, and Node.js.

[1177] Examples and prompts

[1178] For example, for emotion recognition, the following prompt sentences are used:

[1179] Example prompt sentence:

[1180] "If a driver is feeling stressed, identify that emotion in real time and adjust your operation plan."

[1181] Example of a prompt for real-time route generation:

[1182] "Generate the most efficient and safe route, taking into account your current location and weather information."

[1183] This system reduces the burden on drivers while providing optimal logistics routes in real time, achieving efficient logistics and cost reductions. In addition, the emotion engine recognizes the emotional state of drivers and uses this information to adjust operation plans, providing a safer and more comfortable working environment.

[1184] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1185] Step 1:

[1186] The server collects road conditions and weather information in real time. It uses the Google Maps API to obtain traffic information (e.g., traffic congestion information) and the OpenWeatherMap API to obtain the latest weather data. Specifically, it sends the current coordinates as input to each API and stores the obtained traffic and weather information in an internal database.

[1187] Input: Current coordinates

[1188] Output: Latest traffic information, weather data

[1189] Step 2:

[1190] The server uses AI algorithms to analyze road congestion and weather fluctuations based on the collected traffic information and weather data. It also analyzes driver fatigue levels and vehicle conditions obtained from the logistics management system. This allows the AI ​​model to combine each piece of data to create basic data for generating optimal logistics routes.

[1191] Input: Traffic information, weather data, driver fatigue level, vehicle condition

[1192] Output: Analysis results (road congestion, weather fluctuations, driver status)

[1193] Step 3:

[1194] The server generates optimal logistics routes based on the analysis results and creates operation plans that take driver workloads into account. Specifically, the schedule is set taking into account driver rest stops and continuous driving time. The generated operation plan also includes the priority of the next delivery and detour routes.

[1195] Input: Analysis results

[1196] Output: Optimal logistics routes and operation plans

[1197] Step 4:

[1198] The device displays the optimal route and operation plan sent from the server to the driver. The screen also displays the distance to the next right turn and the arrival time based on speed. It also has a function to notify the driver of traffic information and weather forecast updates in real time.

[1199] Input: Optimal logistics route, operation plan

[1200] Output: Display to driver (route guidance, traffic information, weather forecast)

[1201] Step 5:

[1202] The device collects feedback from the driver. When the driver inputs information about road conditions and weather changes into the device, the data is immediately sent to the server. This feedback information is used by the server to improve the route.

[1203] Input: Driver feedback (road conditions, weather changes)

[1204] Output: Send to server, collect feedback information

[1205] Step 6:

[1206] The device's emotion recognition engine recognizes the driver's emotional state in real time. Specifically, it uses the camera and microphone in the smart glasses or head-mounted display to analyze the driver's facial expressions and tone of voice to determine their stress and fatigue levels. This information is recorded on the device as emotion recognition data and sent to a server.

[1207] Input: Driver's facial expression data, voice tone

[1208] Output: Emotion recognition data (stress, fatigue level)

[1209] Step 7:

[1210] The server analyzes the emotion recognition data sent from the device and evaluates the driver's stress and fatigue level. Based on this evaluation result, the operation plan is adjusted. For example, if the driver is feeling very stressed, the planned route may be changed and adjustments made such as increasing rest stops.

[1211] Input: Emotion recognition data

[1212] Output: Adjusted trip plan

[1213] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1214] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1215] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1216] [Fourth embodiment]

[1217] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1218] 7, a 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.

[1219] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1220] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1221] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1222] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1223] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1224] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1225] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1226] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1227] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1228] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1229] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1230] This invention is a new logistics system that utilizes generative AI. This system collects road and weather information in real time and can quickly propose optimal logistics routes. It also provides plans to minimize driver workload, aiming to reduce transportation costs and improve driver working conditions.

[1231] Server Operation

[1232] Data collection

[1233] The server obtains real-time road and weather information from external APIs. This allows it to collect current traffic volume, accident information, weather conditions, etc. It also obtains cargo information, driver work status, and vehicle status from the logistics management system. For example, the server obtains road information from the Google Maps API and weather information from the OpenWeatherMap API, while simultaneously understanding the driver's work status from the traffic management system.

[1234] Data analysis

[1235] The collected information is analyzed on a server. An AI algorithm is used to analyze the collected data and evaluate road congestion, weather fluctuations, and driver fatigue. Delivery priorities are also determined taking into account product characteristics (weight, volume, priority, etc.). For example, an AI model analyzes road congestion and weather fluctuations to generate a delivery plan that prioritizes the delivery of high-priority products.

[1236] Route Optimization

[1237] Based on the analysis results, the server generates an optimal logistics route. The generated route takes into account the driver's workload, avoids long periods of continuous driving, and includes regular rest stops. For example, the server may suggest detour routes to avoid traffic jams and create a plan for efficient delivery by using expressways.

[1238] Device behavior

[1239] Displaying instructions

[1240] The device displays the optimized route and operation plan sent from the server to the driver. It also notifies the driver of traffic information and weather forecast updates in real time. For example, the device displays specific instructions such as "The next right turn is 2 kilometers away. At your current speed, you can reach it within 10 minutes."

[1241] Collecting feedback

[1242] The device collects feedback from the driver. When the driver inputs road conditions or changes in weather into the device, that information is immediately sent to the server, and the route is improved. For example, if the driver inputs "delays due to road construction" into the device, that information is sent to the server and reanalyzed.

[1243] User Actions

[1244] Check the route

[1245] The user (driver) checks the route displayed on the device and starts driving according to the instructions. For example, the user follows the instructions on the device to turn right and proceed to the next destination.

[1246] Providing Feedback

[1247] Users input changes in traffic conditions and weather into their devices. If there is new information, it is immediately entered into the device and sent to the server, which then revises the route. For example, "the user can input that the road is congested into the device, and that data is sent to the server, which then generates a new optimal route."

[1248] This system will reduce the burden on drivers while improving logistics efficiency and reducing costs.

[1249] The processing flow will be explained below.

[1250] Step 1:

[1251] The server collects road conditions and weather information in real time.

[1252] The server periodically obtains current traffic and weather information using external APIs. Specifically, the server uses the Google Maps API to collect road congestion and accident information, and the OpenWeatherMap API to obtain current weather information. The server also obtains cargo information and driver work status from the logistics management system (WMS) and the transportation management system (TMS).

[1253] Step 2:

[1254] The server performs analysis based on the collected data.

[1255] The data collected on the server is analyzed using an AI model. The analysis evaluates road congestion, weather fluctuations, and driver fatigue. Product characteristics (weight, volume, priority) are also taken into account to determine delivery priorities. For example, the server inputs the collected data into the AI ​​to determine whether a detour route is necessary if a particular road is congested.

[1256] Step 3:

[1257] The server generates optimal routes and operation plans.

[1258] Based on the results of the data analysis, the server generates an optimal logistics route that takes into account the driver's workload. The generated route includes rest stops for the driver and a schedule to prevent continuous driving of less than five hours. For example, the server suggests using highways or taking shorter routes to avoid traffic jams.

[1259] Step 4:

[1260] The terminal displays the optimal route and operation plan received from the server to the driver.

[1261] The device receives data from the server and displays the optimal route and trip plan to the driver. It also provides real-time updates on traffic and weather forecasts. For example, the device might display specific instructions such as, "The next right turn is 2 kilometers away. At your current speed, you can reach it within 10 minutes."

[1262] Step 5:

[1263] The user checks the displayed route and starts driving.

[1264] The user (driver) checks the route displayed on the terminal and starts driving according to the instructions, for example, driving the car towards the destination according to the clearly indicated route.

[1265] Step 6:

[1266] The device collects feedback from the driver.

[1267] The device accepts feedback from the driver. When the driver inputs new information into the device, such as traffic conditions or weather changes, the information is immediately sent to the server. For example, if the driver inputs "Delay due to road construction," the information is sent to the server.

[1268] Step 7:

[1269] The server will re-analyze the route based on the new data.

[1270] The server re-analyzes the data based on the feedback sent by the driver. This re-analysis reflects the latest traffic and weather information and re-generates an optimized route. For example, if road construction is reported, the server will suggest a new detour route.

[1271] This series of processing steps provides drivers with logistics routes optimized in real time, reducing the burden on drivers and achieving efficient logistics.

[1272] Example 1

[1273] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1274] Current logistics systems have difficulty effectively utilizing real-time information on road conditions and weather to generate optimal delivery routes, resulting in increased workloads for workers and rising transportation costs. Another issue is the insufficient means for incorporating feedback from workers into the entire system, making it difficult to quickly improve routes.

[1275] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1276] In this invention, the server includes a data acquisition means for collecting road condition and weather information in real time via a digital data interface, a data analysis means for analyzing road congestion, weather fluctuations, and worker fatigue levels based on the collected data, a route optimization means for generating optimal delivery routes based on the results of the data analysis means and creating operation plans that take worker burdens into consideration, and a feedback collection means for dynamically receiving input from workers and improving routes across the entire system. This makes it possible to generate fast and efficient delivery routes based on environmental information that changes in real time, reduce worker burdens, and reduce transportation costs.

[1277] A "digital data interface" is a communication means for collecting information from various data sources in real time.

[1278] "Data acquisition means" is a function that acquires necessary information in real time from external APIs and internal systems.

[1279] "Data analysis means" refers to AI algorithms and related tools for analyzing collected data and predicting and assessing changes in the situation.

[1280] A "route optimization method" is a means for generating optimal logistics routes based on analysis results and creating operation plans that take into account the burden on workers.

[1281] The "feedback collection means" is a function that sends real-time feedback from workers to the server and uses it to reevaluate the system and improve routes.

[1282] "Display means" refers to a device that visually presents optimal routes and operation plans, as well as real-time traffic and weather information to workers.

[1283] "Communication means" refers to a means for transmitting and receiving data packets between the server and the worker's terminal.

[1284] This invention is a system that utilizes generative AI models to improve logistics efficiency and reduce the burden on workers. By linking the elements of the server, terminals, and users, this system generates optimal delivery routes that take into account changing road conditions and weather information in real time, and can adjust operation plans to reflect worker feedback.

[1285] Hardware and software used

[1286] server

[1287] The server collects various data via a digital data interface, analyzes the data using a generative AI model, and performs route optimization. Specifically, it uses the following APIs and software:

[1288] Google Maps API: Obtains real-time road condition data such as traffic volume and accident information.

[1289] OpenWeatherMap API: Get real-time weather information.

[1290] TensorFlow: A library for running AI algorithms required for data analysis.

[1291] Terminal

[1292] The device visually displays the optimal route and operation plan sent from the server, and also provides real-time updates on traffic and weather forecasts and collects feedback from workers.

[1293] GPS navigation software: used to provide route guidance.

[1294] Real-time notification system: Notify workers of traffic and weather updates.

[1295] User (worker)

[1296] The user (worker) drives the vehicle according to the instructions displayed on the terminal and inputs feedback on road conditions and weather changes into the terminal.

[1297] Program processing

[1298] The server collects real-time road and weather information via the Google Maps API and OpenWeatherMap API. It then formats the data and converts it into a consistent format. The collected data is analyzed using a generative AI model (e.g., TensorFlow). Based on the analysis results, the server generates optimal delivery routes and creates operation plans that take into account worker workloads.

[1299] The plan is sent from the server to the device, which displays it visually to the worker. Additionally, traffic and weather updates are communicated to the worker in real time through visual and audio notifications. The worker enters feedback on road conditions and weather changes into the device, which sends this information to the server. The server reanalyzes the route based on the new information and updates the trip plan as needed.

[1300] Specific examples

[1301] For example, the server retrieves traffic information using the Google Maps API and rainfall information using the OpenWeatherMap API. The collected data is analyzed using the TensorFlow library to generate a delivery plan that delivers high-priority items (e.g., medical supplies) first. The terminal displays instructions such as, "The next right turn is 2 kilometers away. At your current speed, you can reach it within 10 minutes."

[1302] Prompt Sentence Examples

[1303] You are a logistics manager. You want to use the latest technology to reduce the workload of your workers and ensure efficient deliveries. There is currently congestion on the roads, but no rain. Please suggest the following optimal route to prioritize the urgent delivery of medical supplies:

[1304] This system will enable the generation of fast and efficient delivery routes based on real-time changing environmental information, reducing the burden on workers and cutting transportation costs.

[1305] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1306] Step 1:

[1307] The server collects road and weather information in real time via a digital data interface. Specifically, it uses the Google Maps API to obtain traffic and accident information, and the OpenWeatherMap API to obtain weather information. The server obtains data by sending parameters such as an API key and location information. The input is a request to the API, and the output is the latest road and weather information.

[1308] Step 2:

[1309] The server converts the collected data into a consistent format. For example, data acquired in JSON format can be converted to CSV format and invalid data can be removed. This preprocessing step also performs missing value imputation and noise removal. The input is the collected raw data, and the output is the formatted data.

[1310] Step 3:

[1311] The server analyzes the preprocessed data using an AI algorithm (e.g., TensorFlow). Specifically, it uses an LSTM model to predict road congestion and weather fluctuations, and performs analysis that takes driver fatigue into account. The input is the formatted data, and the output is the predicted results of road congestion, weather fluctuations, and driver fatigue.

[1312] Step 4:

[1313] The server generates multiple delivery route candidates based on the analysis results. For example, it simulates multiple routes using discrete event simulation. At this stage, the server takes product characteristics (weight, volume, priority) into account and generates a route that prioritizes high-priority products. The inputs are the analysis results and product characteristic data, and the output is multiple route candidates.

[1314] Step 5:

[1315] The server evaluates the generated route candidates and selects the most efficient route that places the least strain on the driver. For example, it evaluates each route using a cost function and determines the optimal route based on parameters such as total travel time and number of rest stops. The input is the route candidates, and the output is the optimal route.

[1316] Step 6:

[1317] The server sends the optimal route and operation plan to the terminal. Specifically, it uses an HTTP POST request to send the operation plan to the terminal as a data packet. The optimal route and operation plan are input, and are sent to the terminal as output.

[1318] Step 7:

[1319] The device receives route information and visually displays it to the driver. For example, it uses a mapping app to display the route on a map and provide turn-by-turn instructions. It also notifies the driver of real-time traffic and weather updates. The input is the route information and trip plan sent from the server, and the output is presented to the driver in the form of visuals and notifications.

[1320] Step 8:

[1321] The user (driver) inputs feedback about road conditions and weather changes into the device. Specifically, feedback such as "road construction" is input using voice input or a touch screen. The input can be single or continuous feedback data, and the output is stored in the device and sent to the server.

[1322] Step 9:

[1323] The terminal immediately sends the collected feedback to the server, for example, by sending the feedback data to the " / feedback / update" endpoint via an HTTP POST request. The input is the feedback data from the driver, and the output is sent to the server.

[1324] Step 10:

[1325] The server reanalyzes the route based on the new feedback information and updates it as necessary. Specifically, it analyzes the feedback information and again uses an AI algorithm to recalculate the optimal route. The input is the new feedback information, and the output is an updated optimal route. This new route is again sent to the device and notified to the driver.

[1326] (Application example 1)

[1327] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1328] Current logistics systems often do not utilize real-time information on road conditions and weather, resulting in frequent delivery delays due to traffic congestion and bad weather. Furthermore, operation plans are often made without taking driver fatigue into account, leaving drivers with no improvement in their working conditions. Furthermore, there is a lack of systems that can immediately respond to changes in road conditions and weather that differ from the current situation, making efficient delivery difficult.

[1329] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1330] In this invention, the server includes a data collection means for collecting road conditions and weather information in real time, a data analysis means for analyzing road congestion, weather fluctuations, and driver fatigue based on the collected data, and a route optimization means for generating an optimal logistics route based on the analysis results and creating an operation plan that takes driver workload into consideration. This makes it possible to propose efficient and rapid logistics routes and reduce driver workload.

[1331] "Data collection means for collecting road conditions and weather information in real time" refers to a device or program for continuously obtaining current road conditions and weather information from an external API, etc.

[1332] "Data analysis means for analyzing road congestion, weather changes, and driver fatigue level based on collected data" refers to a device or program for analyzing collected information and evaluating the current state of road congestion, weather changes, and driver fatigue level.

[1333] "Route optimization means for generating optimal logistics routes based on analysis results and creating operation plans that take driver workload into consideration" refers to a device or program that generates routes and operation plans to optimize delivery efficiency and the driver's working environment based on analyzed data.

[1334] The "feedback receiving means for receiving input of information while driving" is a device or program for collecting feedback information (such as changes in road conditions and weather) from the driver.

[1335] The "re-route generation means for reanalyzing and re-proposing an optimal route based on input information" is a device or program for calculating and proposing a new optimal route based on feedback information from the driver.

[1336] "Display means for displaying the optimal route and operation plan to the driver and notifying him of updates to traffic information and weather forecasts in real time" refers to a device or program that displays the generated route and operation plan, as well as the latest traffic and weather information, in an easy-to-understand manner to the driver.

[1337] The "feedback collection means for collecting feedback from the driver and transmitting it to a server to improve the route" is a device or program for collecting feedback information from the driver and transmitting the information to a server to generate a new optimal route.

[1338] Overall system overview

[1339] This invention is a system that uses a generative AI model to propose optimal logistics routes in real time. The system consists of a server, terminals, and users, all of which work in conjunction with each other. The detailed roles and operations of each component are explained below.

[1340] Server Operation

[1341] Data collection

[1342] The server collects data using the following external APIs:

[1343] Road information: Real-time road conditions are collected from external map service APIs (general name: map information provision APIs).

[1344] Weather information: Obtain real-time weather information from a weather information API.

[1345] Operation management system: Obtains cargo information, driver work status, and vehicle status from logistics management.

[1346] Examples:

[1347] Road information is obtained from the map information API, and weather information is obtained from the weather information API.

[1348] The collected information is stored in the server's disk storage.

[1349] Data analysis

[1350] A generative AI model is used to analyze the collected data.

[1351] Congestion analysis: Evaluate the degree of congestion on roads.

[1352] Weather Variability Analysis: Assess current and forecasted weather.

[1353] Fatigue analysis: Evaluate the driver's fatigue level based on their work status.

[1354] Examples:

[1355] The driver's fatigue level is evaluated from the driving status log.

[1356] A generative AI model analyzes data on congestion and weather fluctuations to generate a delivery plan that prioritizes high-priority items.

[1357] Route Optimization

[1358] Route optimization tools are used to generate optimal routes and operation plans based on the collected and analyzed data.

[1359] Route generation: A generative AI model suggests the optimal route.

[1360] Rest Point Suggestion: Suggest appropriate rest points to avoid long driving times.

[1361] Examples:

[1362] The server suggests the optimal route while taking into account the driver's level of fatigue.

[1363] The generated route includes plans to use expressways.

[1364] Device behavior

[1365] Displaying instructions

[1366] The terminal displays the optimal route and operation plan sent from the server to the driver.

[1367] Real-time notifications: Traffic and weather updates and notifications in real time.

[1368] Examples:

[1369] Specific instructions such as "The next right turn is 2 kilometers away. At your current speed, you can get there in 10 minutes" are displayed on the device.

[1370] Collecting feedback

[1371] Feedback from drivers is collected and sent to the server to improve routes.

[1372] Information input: The driver inputs changes in road conditions and weather.

[1373] Server submission: The input information is immediately sent to the server for re-analysis.

[1374] Examples:

[1375] If a driver feels they are stuck in traffic, they can enter "Stuck in Traffic."

[1376] Based on the input information, a new optimal route is generated by the server and sent back to the terminal.

[1377] User Actions

[1378] Check the route

[1379] The user (driver) checks the route displayed on the terminal and starts driving according to the instructions.

[1380] Examples:

[1381] "The user follows the instructions on the device to turn right and proceed to their next destination."

[1382] Providing Feedback

[1383] Users can input changes in traffic conditions and weather into their devices and have the route revised by sending the information to the server.

[1384] Examples:

[1385] "The user inputs into the device that the road is congested, and the data is sent to the server."

[1386] Prompt Sentence Examples

[1387] Below are some example prompts to be input to the AI ​​model.

[1388] The user's current location is [latitude, longitude] and their destination is [latitude, longitude]. The following road information was obtained from the map information API: ... The following weather information was obtained from the weather information API: ... Please suggest a route for a high-priority delivery based on the current road conditions and weather.

[1389] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1390] Step 1:

[1391] The server collects real-time road conditions and weather information from external APIs. Specifically, it obtains road congestion data from a map information API and weather data from a weather information API. The obtained data is stored in a database. The input is traffic and weather information from the API, and the output is the stored data.

[1392] Step 2:

[1393] The server uses a generative AI model to analyze the collected data. First, it evaluates road congestion, then analyzes weather fluctuations, and finally evaluates the driver's work status and fatigue level. The input is the stored traffic and weather information, and the output is the congestion level of each road, weather fluctuations, and driver fatigue level assessment results.

[1394] Step 3:

[1395] The server uses a generative AI model based on the analysis results to generate the optimal logistics route. It also creates an operation plan that takes into account the driver's workload. This involves generating a route that takes into account the priority of deliveries and includes necessary rest stops. The inputs are the analysis results and delivery information, and the output is the optimal route and operation plan.

[1396] Step 4:

[1397] The terminal receives the optimal route and operation plan sent from the server and displays them to the driver. It also notifies the driver of real-time updates on traffic information and weather forecasts. Specifically, it displays instructions such as "2 kilometers to the next right turn." The input is the optimal route and operation plan, and the output is the notification to the driver.

[1398] Step 5:

[1399] When the driver inputs changes in road conditions or weather into the terminal while driving, the terminal sends that feedback information to the server. The input is feedback information from the driver, and the output is the transmission of that information to the server.

[1400] Step 6:

[1401] Based on the received feedback, the server reanalyzes the route using a generative AI model and proposes a new optimal route. This information is sent to the device in real time. The input is the feedback, and the output is the reanalyzed new optimal route.

[1402] Step 7:

[1403] The terminal receives updated route information from the server and displays the new route to the driver. The input is the new optimal route information, and the output is the new notification content to the driver.

[1404] Step 8:

[1405] The user (driver) then starts driving again according to the new route and inputs further feedback information into the terminal as necessary. This allows the driver and the system to work together to achieve efficient logistics. The input is the new route information and the driver's feedback, and the output is route optimization by the system and improvements to the driver's driving.

[1406] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1407] This invention is a new logistics system that utilizes generative AI to collect road and weather information in real time and propose optimal logistics routes based on that information. Furthermore, to reduce the driver's workload and achieve efficient logistics, it incorporates an emotion engine that recognizes the user's emotions. This system analyzes the driver's emotional state and reflects this in the operation plan, resulting in more precise route optimization.

[1408] Server Operation

[1409] Data collection

[1410] The server obtains real-time road and weather information from external APIs. This allows it to collect current traffic volume, accident information, weather conditions, and more. For example, the server uses the Google Maps API to collect traffic congestion information and the OpenWeatherMap API to obtain weather data. It also obtains cargo information, driver work status, and vehicle status from the logistics management system.

[1411] Data analysis

[1412] The collected information is analyzed on a server. AI algorithms are used to evaluate road congestion, weather fluctuations, and driver fatigue. Delivery priorities are also determined taking into account product characteristics (weight, volume, priority). For example, an AI model analyzes road congestion and weather fluctuations and adjusts delivery schedules to deliver products with high delivery priorities first.

[1413] Route Optimization

[1414] Based on the analysis results, the server generates an optimal logistics route. The generated route takes into account the driver's workload and includes rest stops and a schedule that prevents continuous driving of less than five hours. For example, it suggests a detour route that uses the highway to avoid traffic jams.

[1415] Device behavior

[1416] Displaying instructions

[1417] The device displays the optimized route and operation plan sent from the server to the driver. It also notifies the driver of real-time traffic and weather updates. For example, it displays specific instructions such as "The next right turn is 2 kilometers away. At your current speed, you can reach it within 10 minutes."

[1418] Collecting feedback

[1419] The device collects feedback from the driver. When the driver inputs changes in road conditions or weather into the device, the information is immediately sent to the server. For example, if the driver inputs "Delay due to road construction," the information is sent to the server.

[1420] Emotion Engine Operation

[1421] Emotion recognition

[1422] The device is equipped with an emotion engine that recognizes the driver's emotional state in real time. Using cameras, microphones, and various sensors, it reads emotions from the driver's facial expressions and tone of voice. For example, if the driver is feeling stressed, that information will be detected by the device.

[1423] Emotion Analysis

[1424] The server analyzes the emotion data sent from the device and evaluates the driver's stress and fatigue level. The evaluation results are used to adjust the operation plan. For example, if the emotion analysis shows that the driver is feeling highly stressed, the planned route can be changed and rest stops added.

[1425] User Actions

[1426] Check the route

[1427] The user (driver) checks the route displayed on the terminal and starts driving according to the instructions. For example, the user drives the car from the starting point based on the displayed route.

[1428] Providing Feedback

[1429] Users input information about traffic conditions, weather changes, and emotional states into the device. If there is new information, they input it into the device and the data is sent to the server. For example, if you input "the roads are congested" into the device, the information is immediately sent to the server.

[1430] This system reduces the workload of drivers while providing optimal logistics routes in real time, achieving efficient logistics and cost reduction. In addition, the emotion engine recognizes the driver's emotional state and helps adjust operation plans, providing a safer and more comfortable working environment.

[1431] The processing flow will be explained below.

[1432] Step 1:

[1433] The server collects road conditions and weather information in real time.

[1434] The server periodically obtains current traffic and weather information using external APIs. For example, the server uses the Google Maps API to collect traffic and accident information, and the OpenWeatherMap API to obtain current weather information. The server also obtains cargo information and driver work status from the Warehouse Management System (WMS) and the Transportation Management System (TMS).

[1435] Step 2:

[1436] The server performs analysis based on the collected data.

[1437] The data collected on the server is analyzed using an AI model. The analysis evaluates road congestion, weather fluctuations, and driver fatigue. Delivery priorities are also determined, taking into account product characteristics (weight, volume, priority). For example, the AI ​​model analyzes road congestion and weather fluctuations and determines if detours are necessary.

[1438] Step 3:

[1439] The server generates optimal routes and operation plans.

[1440] Based on the results of the data analysis, the server generates an optimal logistics route that takes into account the driver's workload. This generated route includes a schedule that limits rest stops and continuous driving time. For example, it suggests routes that use highways to avoid traffic jams and plans to insert a 20-minute break after three hours of driving.

[1441] Step 4:

[1442] The terminal displays the optimal route and operation plan received from the server to the driver.

[1443] The device receives data from the server and displays the optimal route and trip plan to the driver. It also provides real-time updates on traffic and weather forecasts. For example, the device might display, "The next right turn is 2 kilometers away, and you can reach it in 10 minutes at your current speed."

[1444] Step 5:

[1445] The user checks the displayed route and starts driving.

[1446] The user (driver) checks the route displayed on the terminal and starts driving according to the instructions. Specifically, the driver drives the vehicle from the departure point to the destination based on the displayed route.

[1447] Step 6:

[1448] The device recognizes the driver's emotions in real time.

[1449] The device uses cameras, microphones, and various sensors to read the driver's emotions from their facial expressions and tone of voice. The emotion engine determines the driver's stress level and fatigue. For example, the device can use the driver's facial recognition camera to detect when the driver is tired.

[1450] Step 7:

[1451] The server performs analysis based on the emotion data.

[1452] The server receives and analyzes the emotion data sent from the device. The server evaluates the driver's stress level and fatigue level and adjusts the trip plan based on the results. For example, if the driver is in a high-stress state, the server will modify the existing trip plan to include additional breaks.

[1453] Step 8:

[1454] The device collects feedback from the driver.

[1455] The device accepts feedback from the driver. When the driver inputs information about road conditions, weather changes, or a new emotional state, that information is immediately sent to the server. For example, if the driver inputs "Delay due to road construction" into the device, that information is sent to the server and reanalyzed.

[1456] Step 9:

[1457] The server will re-analyze the route based on the new data.

[1458] The server then re-analyzes the data based on the feedback and emotion data sent by the driver. This re-analysis reflects the latest traffic and weather information, as well as the driver's emotional state, and re-generates an optimized route. For example, if road construction is reported, the server will suggest a new detour route and set additional rest stops according to the driver's stress level.

[1459] This series of processing steps provides drivers with optimized logistics routes in real time, reducing their workload and achieving efficient logistics. Furthermore, the emotion engine monitors the driver's emotional state in real time and reflects this in the operation plan, ensuring driver safety and effective delivery.

[1460] Example 2

[1461] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1462] Conventional logistics systems often generate inefficient operation plans because they are unable to properly consider changes in road conditions and weather information, as well as driver fatigue levels. It is also difficult to grasp the emotional state of the driver, making it difficult to achieve safe operation and reduce driver burden. There is a need to solve these problems and provide efficient and safe operation plans.

[1463] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1464] In this invention, the server includes a data collection means for collecting road condition and weather information in real time, a data analysis means for analyzing road congestion, weather fluctuations, and driver fatigue based on the collected data, a route optimization means for generating an optimal logistics route based on the analysis results and creating an operation plan that takes driver workload into consideration, and an emotion analysis means for analyzing the driver's emotional state and reflecting it in the operation plan. This makes it possible to generate an efficient and safe operation plan that comprehensively takes into consideration road conditions, weather, and the driver's condition and emotion.

[1465] "Data collection means" means devices and methods for obtaining real-time road condition and weather information from external sources.

[1466] "Data analysis means" refers to a method and device that uses AI algorithms to analyze road congestion, weather fluctuations, and driver fatigue levels based on collected road and weather information.

[1467] The "route optimization means" is a method and device for generating an optimal logistics route and creating an operation plan while taking into consideration the driver's workload, based on the analysis results obtained by the data analysis means.

[1468] The "emotion analysis means" is a method and device for monitoring and analyzing the driver's emotional state and reflecting it in the operation plan.

[1469] "Display means" refers to a device and method that displays optimal routes and operation plans to drivers and notifies them of traffic information and weather forecast updates in real time.

[1470] "Feedback collection means" refers to devices and methods for collecting driver feedback and transmitting it to a server for route improvement.

[1471] This system collects road and weather information in real time and proposes optimal logistics routes based on that information. Furthermore, it analyzes the driver's emotional state and reflects it in the operation plan, achieving more precise route optimization.

[1472] Server Operation

[1473] Data collection methods

[1474] The server uses the Google Maps API to obtain real-time road information, which allows it to collect traffic volume, accident information, and congestion information. It also uses the OpenWeatherMap API to obtain weather data and obtain current weather and forecasts. Furthermore, it collects vehicle load information, driver work status, and vehicle condition information from the logistics management system.

[1475] Data Analysis Methods

[1476] The server uses collected road and weather information to perform data analysis using AI algorithms. Specifically, it evaluates road congestion, accident rates, and weather fluctuations. It determines delivery priorities by taking into account the driver's work schedule, vehicle condition, and cargo information.

[1477] Route optimization methods

[1478] The server then generates optimal logistics routes based on the results of the data analysis. These routes take into account the driver's workload and include rest stops and a schedule that limits continuous driving to five hours. For example, it suggests detour routes, including using highways to avoid traffic jams.

[1479] Emotion analysis means

[1480] The server analyzes the driver's emotional data, including facial expressions and tone of voice, sent from the device. This data is used to evaluate the driver's stress and fatigue level, and the results are reflected in the operation plan. Based on the results of the driver's emotional analysis, the server can increase rest stops or readjust the route as necessary.

[1481] Device behavior

[1482] Display means

[1483] The device displays the optimized route and operation plan sent from the server to the driver. It also notifies the driver of real-time traffic and weather updates while driving. For example, it displays specific instructions such as, "The next right turn is 2 kilometers away. At your current speed, you can reach it within 10 minutes."

[1484] Feedback collection methods

[1485] The device collects feedback from the driver: if the driver types in "delay due to roadworks," the information is immediately sent to the server, which recalculates the route.

[1486] User Actions

[1487] Check the route

[1488] The user checks the route displayed on the terminal and starts driving according to the instructions, for example, by moving the car from the starting point based on the displayed route.

[1489] Providing Feedback

[1490] Users input information about traffic conditions, weather changes, and their emotional state into the device. For example, if they input "the roads are congested," that information is immediately sent to the server, and the route is recalculated.

[1491] Specific examples

[1492] 1. The server uses the Google Maps API to obtain real-time traffic information.

[1493] 2. The server analyzes the collected information and the driver's work status to generate a driving plan including the optimal route and rest points.

[1494] 3. The device will display instructions to the driver saying, "The next right turn is 2 kilometers away. At your current speed, you can get there in 10 minutes."

[1495] 4. When a driver types "the road is congested" into the device, that information is immediately sent to the server and the route is recalculated.

[1496] Example prompts for generative AI models

[1497] "How can I get real-time traffic information for a specific location using the Google Maps API?"

[1498] "Please provide an example of an algorithm for recognizing emotions from a driver's facial expressions and tone of voice."

[1499] This system reduces the workload of drivers, enabling efficient logistics and cost reduction. It also uses an emotion engine to understand the driver's emotional state and use this information to adjust operation plans, providing a safer and more comfortable working environment.

[1500] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1501] Server Operation Steps

[1502] Step 1: Data collection

[1503] The server obtains real-time road information (traffic volume, accident information, congestion information) through the Google Maps API. The input is the coordinate information of a specific location, and the output is road condition data for the corresponding location. This allows the current traffic situation to be understood.

[1504] The server uses the OpenWeatherMap API to obtain weather data (current weather and forecast). The input is the coordinate information of a location, and the output is the weather information for that location. This allows us to determine the weather conditions that will affect logistics routes.

[1505] Furthermore, the server obtains information on the vehicle's cargo, the driver's work status, the vehicle's condition, etc. from the logistics management system. The input is various data related to the operation plan, and the output is a data set that aggregates this information.

[1506] Specific operation: The server sends a request to the Google Maps API every minute, collects road information, stores it in a database, and updates weather data every hour using the OpenWeatherMap API.

[1507] Step 2: Data analysis

[1508] The server analyzes the collected road and weather information using an AI algorithm. The input is the dataset collected in step 1, and the output is the analysis results regarding road congestion, accident rates, and weather fluctuations. This allows for the evaluation necessary to select logistics routes.

[1509] The server determines delivery priorities based on the driver's work schedule, vehicle status, and cargo information. The inputs are driver work schedule data, vehicle status data, and product data, and the output is a list of products with high delivery priority.

[1510] How it works: AI algorithms analyze data and calculate the optimal route based on current traffic and weather conditions, then select items with high delivery priority and plan the route accordingly.

[1511] Step 3: Route optimization

[1512] Based on the analysis results, the server generates the optimal logistics route taking into account the driver's fatigue level and stress. The input is the analysis results obtained in step 2 and the product list, and the output is the optimized logistics route.

[1513] The route will include rest stops and a schedule that limits continuous operation to five hours, allowing for safe and efficient operation planning.

[1514] Specific operation: Based on the results of the driver's emotion analysis, the server reevaluates the planned route and adds rest stops as necessary. To avoid traffic jams, the server suggests alternative routes, including using the highway.

[1515] Device operation steps

[1516] Step 1: View instructions

[1517] The terminal displays the optimal route and operation plan sent from the server to the driver. The input is the operation plan data from the server, and the output is a display of specific instructions to the driver.

[1518] It provides real-time traffic and weather updates while driving, keeping drivers up to date with the latest information.

[1519] Specific actions: Specific instructions are displayed, such as "The next right turn is 2 kilometers away. At your current speed, you can reach it within 10 minutes." The device also notifies you in real time, "Heavy rain is currently forecast ahead. Please drive safely."

[1520] Step 2: Gather feedback

[1521] The terminal collects feedback from the driver. The input is the feedback information that the driver enters into the terminal, and the output is the process of sending that information to the server.

[1522] For example, if you enter "Delay due to road construction," that information is immediately sent to the server and the route is recalculated.

[1523] Specific operation: When the driver inputs into the device, "The current road conditions are more congested than expected," the device immediately sends that information to the server, which then recalculates the route.

[1524] Emotion Engine Operation Steps

[1525] Step 1: Recognize emotions

[1526] The device uses a camera, microphone, and various sensors to recognize the driver's emotional state in real time. The input is emotional data from the camera and microphone, and the output is the display of the emotional data on the device.

[1527] It assesses the driver's real-time emotional state by reading emotions from their facial expressions and tone of voice.

[1528] How it works: The camera detects signs of stress from the driver's facial expression, and the microphone detects fatigue from the driver's tone of voice.

[1529] Step 2: Sentiment Analysis

[1530] The server analyzes the emotion data sent from the terminal and evaluates the driver's stress and fatigue level. The input is emotion data, and the output is the analyzed emotion evaluation result.

[1531] The results of the evaluation are used to adjust the operation plan and make necessary changes based on the driver's condition.

[1532] Specific behavior: If the server determines that the driver's stress level is high, it will immediately recalculate the route and increase the number of rest points.

[1533] User action steps

[1534] Step 1: Verify the route

[1535] The user checks the route displayed on the terminal and starts driving according to the instructions. The input is the operation plan displayed on the terminal, and the output is the specific actions to be taken.

[1536] Efficient operation is possible by having the vehicle proceed from the starting point based on the displayed route.

[1537] Specific operation: The user follows the instructions on the device and proceeds along the displayed route.

[1538] Step 2: Provide feedback

[1539] The user inputs information about traffic conditions, weather changes, and emotional state into the terminal. The input is the feedback information that the driver inputs into the terminal, and the output is the process of sending that information to the server.

[1540] For example, if you enter "the road is congested," that information is immediately sent to the server and the route is recalculated.

[1541] Specific operation: When the user types "The weather has suddenly changed and it has started to snow" into the device, that information is sent to the server, which then recalculates the route.

[1542] Example prompts for generative AI models

[1543] "How can I get real-time traffic information for a specific location using the Google Maps API?"

[1544] "Please provide an example of an algorithm for recognizing emotions from a driver's facial expressions and tone of voice."

[1545] (Application example 2)

[1546] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1547] In modern logistics systems, it is important to generate optimal logistics routes by responding to real-time changes in road conditions and weather information. Furthermore, to reduce driver workload and achieve efficient delivery, it is necessary to recognize the driver's emotional state and reflect it in the operation plan. However, conventional systems that process these factors in an integrated manner lack real-time information updates and driver emotional analysis, making it difficult to provide a safe and efficient working environment.

[1548] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1549] In this invention, the server includes a data collection means for collecting road condition and weather information in real time, a data analysis means for analyzing road congestion, weather fluctuations, and driver fatigue levels based on the collected data, a route optimization means for generating an optimal logistics route based on the analysis results and creating an operation plan that takes driver workload into consideration, an emotion recognition means for recognizing and analyzing the emotional state of the driver, and an operation plan adjustment means for adjusting the operation plan based on the emotional state of the driver. This enables real-time information updates and driver emotion analysis, making it possible to realize a safe and efficient logistics system.

[1550] "Data collection means" means a device or system that collects road condition and weather information in real time.

[1551] "Data analysis means" refers to a device or system that analyzes road congestion, weather fluctuations, and driver fatigue based on collected data.

[1552] "Route optimization means" refers to a device or system that generates optimal logistics routes based on the results of data analysis and creates operation plans that take driver workload into consideration.

[1553] "Emotion recognition means" refers to a device or system for recognizing and analyzing the emotional state of a driver.

[1554] "Operation plan adjustment means" refers to a device or system that adjusts the operation plan based on the emotional state of the driver.

[1555] "Display means" refers to a device or system that displays the optimum route and operation plan to the driver and notifies them of traffic information and weather forecast updates in real time.

[1556] "Feedback collection means" refers to a device or system that collects feedback from drivers and transmits it to a server to improve the route.

[1557] This invention is a new logistics system that utilizes generative AI, which collects road and weather information in real time and proposes optimal logistics routes based on that information. Furthermore, to reduce the burden on drivers and achieve efficient logistics, it combines an emotion engine that recognizes the user's emotions. To realize this system, it has the following functions:

[1558] Server Operation

[1559] Data collection

[1560] The server obtains real-time road and weather information from external APIs. This allows it to collect current traffic volume, accident information, weather conditions, and more. Specifically, the server uses Google Maps API to collect traffic congestion information and OpenWeatherMap API to obtain weather data. It also obtains cargo information, driver work status, and vehicle status from the logistics management system.

[1561] Data analysis

[1562] The collected information is analyzed on a server. AI algorithms are used to evaluate road congestion, weather fluctuations, and driver fatigue. Delivery priorities are also determined taking into account product characteristics (weight, volume, priority). For example, an AI model analyzes road congestion and weather fluctuations and adjusts delivery schedules to deliver products with high delivery priorities first.

[1563] Route Optimization

[1564] Based on the analysis results, the server generates an optimal logistics route. The generated route takes into account the driver's workload and includes rest stops and a schedule that prevents continuous driving of less than five hours. For example, it suggests a detour route that uses the highway to avoid traffic jams.

[1565] Device behavior

[1566] Displaying instructions

[1567] The device displays the optimized route and operation plan sent from the server to the driver. It also notifies the driver of real-time traffic and weather updates. For example, it displays specific instructions such as "The next right turn is 2 kilometers away. At your current speed, you can reach it within 10 minutes."

[1568] Collecting feedback

[1569] The device collects feedback from the driver. When the driver inputs changes in road conditions or weather into the device, the information is immediately sent to the server. For example, if the driver inputs "Delay due to road construction," the information is sent to the server.

[1570] Emotion Engine Operation

[1571] Emotion recognition

[1572] The device is equipped with an emotion engine that recognizes the driver's emotional state in real time. Using cameras, microphones, and various sensors, it reads emotions from the driver's facial expressions and tone of voice. For example, if the driver is feeling stressed, that information will be detected by the device.

[1573] Emotion Analysis

[1574] The server analyzes the emotion data sent from the device and evaluates the driver's stress and fatigue level. The evaluation results are used to adjust the operation plan. For example, if the emotion analysis shows that the driver is feeling highly stressed, the planned route can be changed and rest stops added.

[1575] Hardware and software used

[1576] The main hardware used is a smartphone, smart glasses, a head-mounted display, and a server, while the software used includes Google Maps API, OpenWeatherMap API, Emotion API, TensorFlow, React Native, and Node.js.

[1577] Examples and prompts

[1578] For example, for emotion recognition, the following prompt sentences are used:

[1579] Example prompt sentence:

[1580] "If a driver is feeling stressed, identify that emotion in real time and adjust your operation plan."

[1581] Example of a prompt for real-time route generation:

[1582] "Generate the most efficient and safe route, taking into account your current location and weather information."

[1583] This system reduces the burden on drivers while providing optimal logistics routes in real time, achieving efficient logistics and cost reductions. In addition, the emotion engine recognizes the emotional state of drivers and uses this information to adjust operation plans, providing a safer and more comfortable working environment.

[1584] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1585] Step 1:

[1586] The server collects road conditions and weather information in real time. It uses the Google Maps API to obtain traffic information (e.g., traffic congestion information) and the OpenWeatherMap API to obtain the latest weather data. Specifically, it sends the current coordinates as input to each API and stores the obtained traffic and weather information in an internal database.

[1587] Input: Current coordinates

[1588] Output: Latest traffic information, weather data

[1589] Step 2:

[1590] The server uses AI algorithms to analyze road congestion and weather fluctuations based on the collected traffic information and weather data. It also analyzes driver fatigue levels and vehicle conditions obtained from the logistics management system. This allows the AI ​​model to combine each piece of data to create basic data for generating optimal logistics routes.

[1591] Input: Traffic information, weather data, driver fatigue level, vehicle condition

[1592] Output: Analysis results (road congestion, weather fluctuations, driver status)

[1593] Step 3:

[1594] The server generates optimal logistics routes based on the analysis results and creates operation plans that take driver workloads into account. Specifically, the schedule is set taking into account driver rest stops and continuous driving time. The generated operation plan also includes the priority of the next delivery and detour routes.

[1595] Input: Analysis results

[1596] Output: Optimal logistics routes and operation plans

[1597] Step 4:

[1598] The device displays the optimal route and operation plan sent from the server to the driver. The screen also displays the distance to the next right turn and the arrival time based on speed. It also has a function to notify the driver of traffic information and weather forecast updates in real time.

[1599] Input: Optimal logistics route, operation plan

[1600] Output: Display to driver (route guidance, traffic information, weather forecast)

[1601] Step 5:

[1602] The device collects feedback from the driver. When the driver inputs information about road conditions and weather changes into the device, the data is immediately sent to the server. This feedback information is used by the server to improve the route.

[1603] Input: Driver feedback (road conditions, weather changes)

[1604] Output: Send to server, collect feedback information

[1605] Step 6:

[1606] The device's emotion recognition engine recognizes the driver's emotional state in real time. Specifically, it uses the camera and microphone in the smart glasses or head-mounted display to analyze the driver's facial expressions and tone of voice to determine their stress and fatigue levels. This information is recorded on the device as emotion recognition data and sent to a server.

[1607] Input: Driver's facial expression data, voice tone

[1608] Output: Emotion recognition data (stress, fatigue level)

[1609] Step 7:

[1610] The server analyzes the emotion recognition data sent from the device and evaluates the driver's stress and fatigue level. Based on this evaluation result, the operation plan is adjusted. For example, if the driver is feeling very stressed, the planned route may be changed and adjustments made such as increasing rest stops.

[1611] Input: Emotion recognition data

[1612] Output: Adjusted trip plan

[1613] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1614] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1615] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1616] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1617] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1618] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1619] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1620] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1621] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1622] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1623] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1624] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1625] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1626] 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.

[1627] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1628] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1629] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1630] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1631] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1632] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1633] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1634] The following is further disclosed regarding the above embodiment.

[1635] (Claim 1)

[1636] a data collection means for collecting real-time road condition and weather information;

[1637] a data analysis means for analyzing road congestion, weather fluctuations, and driver fatigue levels based on the collected data;

[1638] A route optimization method that generates optimal logistics routes based on the analysis results and creates operation plans that take driver workloads into consideration;

[1639] A system including:

[1640] (Claim 2)

[1641] 10. The system of claim 1, further comprising display means for displaying optimal routes and trip plans to the driver and notifying them of real-time traffic and weather updates.

[1642] (Claim 3)

[1643] 10. The system of claim 1, further comprising a feedback collection means for collecting feedback from the driver and transmitting it to the server for route improvement.

[1644] "Example 1"

[1645] (Claim 1)

[1646] data acquisition means for collecting road condition and weather information in real time via a digital data interface;

[1647] a data analysis means for analyzing road congestion, weather fluctuations, and worker fatigue levels based on the collected data;

[1648] A route optimization means for generating optimal delivery routes based on the results of the data analysis means and creating operation plans that take into account the burden on workers;

[1649] a feedback collection mechanism for dynamically receiving worker input and making system-wide route improvements;

[1650] A system including:

[1651] (Claim 2)

[1652] 10. The system of claim 1, further comprising display means for displaying optimal routes and trip plans to workers and notifying them of real-time traffic and weather updates.

[1653] (Claim 3)

[1654] The system according to claim 1, further comprising a communication means for transmitting the route generation result to a terminal of a worker and obtaining feedback from the worker.

[1655] "Application Example 1"

[1656] (Claim 1)

[1657] a data collection means for collecting real-time road condition and weather information;

[1658] a data analysis means for analyzing road congestion, weather fluctuations, and driver fatigue levels based on the collected data;

[1659] A route optimization method that generates optimal logistics routes based on the analysis results and creates operation plans that take driver workloads into consideration;

[1660] a feedback receiving means for receiving input of information while driving;

[1661] a re-route generation means for re-analyzing and re-proposing an optimal route based on the input information;

[1662] A system including:

[1663] (Claim 2)

[1664] 10. The system of claim 1, further comprising display means for displaying optimal routes and trip plans to the driver and notifying them of real-time traffic and weather updates.

[1665] (Claim 3)

[1666] 10. The system of claim 1, further comprising a feedback collection means for collecting feedback from the driver and transmitting it to the server for route improvement.

[1667] "Example 2: Combining Emotion Engines"

[1668] (Claim 1)

[1669] a data collection means for collecting real-time road condition and weather information;

[1670] a data analysis means for analyzing road congestion, weather fluctuations, and driver fatigue levels based on the collected data;

[1671] A route optimization method that generates optimal logistics routes based on the analysis results and creates operation plans that take driver workloads into consideration;

[1672] An emotion analysis means for analyzing the driver's emotional state and reflecting it in the operation plan;

[1673] A system including:

[1674] (Claim 2)

[1675] 10. The system of claim 1, further comprising display means for displaying optimal routes and trip plans to the driver and notifying them of real-time traffic and weather updates.

[1676] (Claim 3)

[1677] 10. The system of claim 1, further comprising a feedback collection means for collecting feedback from the driver and transmitting it to the server for route improvement.

[1678] "Application example 2 when combining emotion engines"

[1679] (Claim 1)

[1680] a data collection means for collecting real-time road condition and weather information;

[1681] a data analysis means for analyzing road congestion, weather fluctuations, and driver fatigue levels based on the collected data;

[1682] A route optimization method that generates optimal logistics routes based on the analysis results and creates operation plans that take driver workloads into consideration;

[1683] emotion recognition means for recognizing and analyzing the emotional state of the driver;

[1684] a trip plan adjustment means for adjusting the trip plan based on the emotional state of the driver;

[1685] A system including:

[1686] (Claim 2)

[1687] 10. The system of claim 1, further comprising display means for displaying optimal routes and trip plans to the driver and notifying them of real-time traffic and weather updates.

[1688] (Claim 3)

[1689] 10. The system of claim 1, further comprising a feedback collection means for collecting feedback from the driver and transmitting it to the server for route improvement. [Explanation of symbols]

[1690] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a data collection means for collecting real-time road condition and weather information; a data analysis means for analyzing road congestion, weather fluctuations, and driver fatigue levels based on the collected data; A route optimization method that generates optimal logistics routes based on the analysis results and creates operation plans that take driver workloads into consideration; A system including:

2. 10. The system of claim 1, further comprising display means for displaying optimal routes and trip plans to the driver and notifying them of real-time traffic and weather updates.

3. 10. The system of claim 1, further comprising a feedback collection means for collecting feedback from the driver and transmitting it to the server for route improvement.

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