System

Abstract

The object and advantages of the invention will be realized and attained by means of the elements and combinations particularly pointed out in the claims.SOLUTION: A system includes a monitoring unit, a prediction unit, and an operation unit. The monitoring unit monitors a position of a vehicle, a traveling situation, traffic jam information, and a fuel consumption amount. The prediction unit performs demand prediction and delivery route optimization processing on the basis of the information monitored by the monitoring unit. The driving unit automatically drives the truck based on the result obtained by the prediction unit.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] With conventional technology, there is room for improvement as it is difficult to address driver shortages in logistics and secure efficient delivery routes.

[0005] The system according to the embodiment aims to resolve the driver shortage in logistics and ensure efficient delivery routes. [Means for solving the problem]

[0006] The system according to the embodiment includes a monitoring unit, a prediction unit, and a driving unit. The monitoring unit monitors the vehicle's location, driving status, traffic congestion information, and fuel consumption. The prediction unit performs demand prediction and delivery route optimization processing based on the information monitored by the monitoring unit. The driving unit automatically drives the truck based on the results obtained by the prediction unit. [Effects of the Invention]

[0007] The system according to the embodiment can resolve the driver shortage in logistics and ensure efficient delivery routes. [Brief explanation of the drawings]

[0008] [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. DETAILED DESCRIPTION OF THE INVENTION

[0009] 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.

[0010] First, the terms used in the following description will be explained.

[0011] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] 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.

[0013] 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.

[0014] 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), and Bluetooth (registered trademark).

[0015] 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."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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).

[0019] 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.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. 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 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. 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.

[0022] 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.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A system according to an embodiment of the present invention uses AI to automatically drive and remotely monitor trucks. This system monitors vehicle location, driving conditions, traffic congestion information, and fuel consumption, and then performs demand forecasting and optimizes delivery routes to automatically drive trucks. This system can solve the Logistics 2024 problem of driver shortages. For example, the system uses AI to analyze information obtained from cameras and sensors mounted on vehicles and grasp the vehicle's status in real time. Next, the AI ​​predicts demand based on past delivery data and current traffic conditions and calculates the optimal delivery route. Furthermore, the AI ​​controls the truck's driving and drives it to its destination. This allows the system to achieve efficient logistics and reduce the burden on drivers.

[0029] An autonomous driving system according to an embodiment includes a monitoring unit, a prediction unit, and a driving unit. The monitoring unit monitors the vehicle's location, driving conditions, traffic congestion information, and fuel consumption. For example, the monitoring unit analyzes information obtained from cameras and sensors mounted on the vehicle to grasp the vehicle's status in real time. The monitoring unit can also acquire vehicle location information from GPS data and analyze driving conditions from camera footage. The monitoring unit can also acquire fuel consumption from sensors to support efficient driving. The prediction unit performs demand forecasting and delivery route optimization processing based on the information monitored by the monitoring unit. For example, the prediction unit performs demand forecasting based on past delivery data and current traffic conditions and calculates an optimal delivery route. The prediction unit can also perform demand forecasting using statistical models and machine learning algorithms. The driving unit automatically drives the truck based on the results obtained by the prediction unit. For example, the driving unit selects an optimal speed and route based on the vehicle's location information and traffic conditions to drive safely. The driving unit can control the truck's driving using autonomous driving technology and drive it to its destination. As a result, the autonomous driving system according to the embodiment can monitor vehicle location, driving conditions, traffic congestion information, and fuel consumption, perform demand forecasts, optimize delivery routes, and drive trucks automatically.

[0030] The monitoring unit analyzes information obtained from cameras and sensors mounted on the vehicle and can grasp the vehicle's status in real time. The monitoring unit, for example, analyzes video data obtained from cameras mounted on the vehicle to grasp the vehicle's driving status. The monitoring unit can also analyze data obtained from sensors to obtain fuel consumption and vehicle location information. Furthermore, the monitoring unit can grasp the vehicle's location in real time using GPS data. This allows the monitoring unit to grasp the vehicle's status in real time. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data obtained from cameras and sensors into AI, which can analyze the data to grasp the vehicle's status.

[0031] The prediction unit can perform demand prediction based on past delivery data and current traffic conditions and calculate an optimized delivery route. The prediction unit, for example, analyzes past delivery data to perform demand prediction. For example, the prediction unit can analyze demand trends based on delivery data from the past year. The prediction unit can also calculate an optimal delivery route based on current traffic conditions. For example, the prediction unit can select an optimal route based on data obtained from real-time traffic sensors. Furthermore, the prediction unit can perform demand prediction using statistical models and machine learning algorithms and calculate an optimal delivery route. This allows the prediction unit to perform demand prediction and calculate an optimal delivery route. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input past delivery data and current traffic conditions into AI, which can then perform demand prediction and calculate an optimal delivery route.

[0032] The driving unit can select an optimized speed and route based on the vehicle's location information and traffic conditions, and drive safely. The driving unit, for example, selects an optimal speed based on the vehicle's location information. For example, the driving unit can grasp the vehicle's location using GPS data and calculate an optimal speed. The driving unit can also select an optimal route based on traffic conditions. For example, the driving unit can select an optimal route based on data obtained from real-time traffic sensors. Furthermore, the driving unit can control the driving of the truck using autonomous driving technology and drive safely. This allows the driving unit to select an optimal speed and route and drive safely. Some or all of the above-mentioned processing in the driving unit may be performed using, for example, AI, or may be performed without using AI. For example, the driving unit inputs the vehicle's location information and traffic conditions into AI, which then calculates the optimal speed and route, and drives safely.

[0033] The monitoring unit can acquire fuel consumption data from a sensor and support efficient driving. The monitoring unit, for example, analyzes data obtained from a fuel sensor to determine fuel consumption. For example, the monitoring unit can calculate fuel consumption based on data obtained from the vehicle's on-board computer. The monitoring unit can also monitor fuel consumption in real time and support efficient driving. For example, the monitoring unit can suggest a driving method that maximizes fuel efficiency. Furthermore, the monitoring unit can suggest a driving method that minimizes fuel consumption using eco-driving technology. In this way, the monitoring unit can acquire fuel consumption data and support efficient driving. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data obtained from the fuel sensor into AI, which can calculate fuel consumption and suggest an efficient driving method.

[0034] The system includes a data collection unit that collects data to be used for demand forecasting and delivery route optimization. The data collection unit, for example, collects data from sensors and stores it in a cloud database. For example, the data collection unit can collect data obtained from sensors mounted on vehicles in real time and store it in a cloud database. The data collection unit also collects past delivery data and uses it for demand forecasting and delivery route optimization. For example, the data collection unit can collect delivery data from the past year and use it for demand forecasting. Furthermore, the data collection unit can collect data obtained from real-time traffic sensors and use it for delivery route optimization. In this way, the data collection unit can collect data to be used for demand forecasting and delivery route optimization. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit inputs data obtained from sensors into AI, which analyzes the data and uses it for demand forecasting and delivery route optimization.

[0035] The system includes a safety measure unit that describes specific details of the autonomous driving technology and safety measures. The safety measure unit, for example, describes the specific details of the autonomous driving technology. For example, the safety measure unit may describe the use of LIDAR, cameras, radar, etc. The safety measure unit may also describe specific details of the safety measures. For example, the safety measure unit may describe the use of a collision avoidance system or an emergency braking system. The safety measure unit may also describe standards for the autonomous driving technology. For example, the safety measure unit may describe standards for level 3 autonomous driving and level 4 autonomous driving. This allows the safety measure unit to describe the specific details of the autonomous driving technology and safety measures. Some or all of the above-described processing in the safety measure unit may be performed using, for example, AI, or may be performed without using AI. For example, the safety measure unit may input the details of the autonomous driving technology and safety measures into AI, which may analyze the information and propose optimal safety measures.

[0036] The system includes a providing unit that provides information to clarify the background and issues of the Logistics 2024 Problem. The providing unit provides information to clarify the background and issues of the Logistics 2024 Problem, for example. For example, the providing unit can provide background information such as labor shortages and stricter environmental regulations. The providing unit can also provide specific issues of the Logistics 2024 Problem. For example, the providing unit can provide issues such as driver shortages and rising logistics costs. The providing unit can also provide solutions to the Logistics 2024 Problem. For example, the providing unit can propose the introduction of autonomous driving technology or remote monitoring technology using AI. This allows the providing unit to provide information to clarify the background and issues of the Logistics 2024 Problem. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input information about the background and issues of the Logistics 2024 Problem into AI, which can then analyze the information and provide optimal information.

[0037] The monitoring unit can improve monitoring accuracy by taking weather information into account when acquiring vehicle position information. The monitoring unit, for example, acquires weather information from a weather data provider and corrects the vehicle position information. For example, the monitoring unit can apply a correction algorithm because the accuracy of GPS signals decreases during rainy weather. The monitoring unit can also correct the position information by taking into account the slipperiness of roads on snowy days. Furthermore, the monitoring unit can acquire highly accurate position information using normal GPS signals during sunny weather. This allows the monitoring unit to improve monitoring accuracy by taking weather information into account. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input weather information into AI, which then corrects the position information to improve monitoring accuracy.

[0038] When monitoring the driving conditions, the monitoring unit can perform analysis taking into account the road conditions. The monitoring unit, for example, detects the road pavement conditions using a sensor and analyzes the driving conditions. For example, the monitoring unit can analyze vibration data and evaluate road unevenness and pavement conditions. The monitoring unit can also analyze the driving conditions taking into account the road gradient. For example, on a road with a steep gradient, the monitoring unit can analyze the driving conditions taking into account the engine load. Furthermore, when the road conditions are good, the monitoring unit can perform analysis based on normal driving data. This allows the monitoring unit to analyze the driving conditions taking into account the road conditions. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input road condition data into AI, which can then analyze the driving conditions.

[0039] The monitoring unit can support efficient driving by taking into account the vehicle load when monitoring fuel consumption. The monitoring unit, for example, detects the vehicle load using a sensor and monitors fuel consumption. For example, the monitoring unit can suggest an efficient driving method because fuel consumption increases when the load is heavy. Furthermore, the monitoring unit can suggest a driving method that minimizes fuel consumption when the load is light. Furthermore, the monitoring unit can monitor fuel consumption in real time according to fluctuations in the load and suggest an optimal driving method. This allows the monitoring unit to support efficient driving by taking into account the vehicle load. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input load data to AI, which monitors fuel consumption and suggests an efficient driving method.

[0040] When acquiring vehicle position information, the monitoring unit can determine the relative position by taking into account the position information of other vehicles. For example, the monitoring unit acquires position information of other vehicles in real time and determines the relative position. For example, the monitoring unit can acquire position information of other vehicles using V2V communication and determine the relative position. The monitoring unit can also acquire position information of other vehicles based on information from a traffic data provider and determine the relative position. Furthermore, the monitoring unit can calculate the distance to other vehicles and support safe driving. For example, the monitoring unit can monitor the distance to other vehicles in real time and select a safe driving route. This allows the monitoring unit to determine the relative position by taking into account the position information of other vehicles. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input position information of other vehicles into AI, which can determine the relative position.

[0041] When monitoring the driving conditions, the monitoring unit can analyze vehicle vibration data to evaluate the road condition. The monitoring unit, for example, acquires vehicle vibration data using a sensor and evaluates the road condition. For example, the monitoring unit can analyze the vibration data to evaluate road unevenness and pavement condition. The monitoring unit can also evaluate the slipperiness of the road from the vibration data to support safe driving. Furthermore, the monitoring unit can identify areas requiring road maintenance based on the vibration data. For example, the monitoring unit can analyze the vibration data to identify areas requiring road maintenance. This allows the monitoring unit to analyze the vehicle vibration data and evaluate the road condition. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the vibration data to AI, which can evaluate the road condition.

[0042] When monitoring fuel consumption, the monitoring unit can optimize fuel efficiency by taking into account the engine operating state. The monitoring unit, for example, acquires engine rotation speed and load using a sensor and monitors fuel consumption. For example, the monitoring unit can calculate fuel consumption based on the engine rotation speed and load and optimize fuel efficiency. The monitoring unit can also analyze the engine operating state in real time and suggest an efficient driving method. Furthermore, the monitoring unit can optimize fuel efficiency by taking into account the engine maintenance state. For example, the monitoring unit can monitor fuel consumption based on the engine maintenance state and suggest an optimal driving method. This allows the monitoring unit to optimize fuel efficiency by taking into account the engine operating state. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input engine operating state data to AI, which can monitor fuel consumption and suggest an efficient driving method.

[0043] The prediction unit can perform demand forecasting taking seasonal fluctuations into account when analyzing past delivery data. The prediction unit, for example, analyzes past delivery data and performs demand forecasting taking seasonal demand fluctuations into account. For example, the prediction unit can analyze seasonal demand patterns based on delivery data from the past year. The prediction unit can also perform demand forecasting taking into account the effects of temperature and weather. For example, the prediction unit can adjust the demand forecast based on temperature and weather data. Furthermore, the prediction unit can predict demand for specific products and services by season. This allows the prediction unit to perform demand forecasting taking seasonal fluctuations into account. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input past delivery data, temperature, and weather data into AI, which can then perform demand forecasting taking seasonal fluctuations into account.

[0044] The prediction unit can predict the risk of an accident based on current traffic conditions and optimize the delivery route. The prediction unit can predict the risk of an accident based on, for example, current traffic congestion information. For example, the prediction unit can evaluate the risk of an accident based on data obtained from real-time traffic sensors. The prediction unit can also predict the risk of an accident based on past accident data. For example, the prediction unit can analyze past accident data and predict the risk of an accident by comparing it with current traffic conditions. Furthermore, the prediction unit can propose an optimal delivery route that avoids routes with a high risk of accidents. This allows the prediction unit to predict the risk of an accident and optimize the delivery route. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input current traffic conditions and past accident data into AI, which can predict the risk of an accident and propose an optimal delivery route.

[0045] When performing demand forecasting, the prediction unit can improve the accuracy of the forecast by taking into account consumption trends for each region. The prediction unit, for example, analyzes consumption trends for each region and reflects the results in the demand forecast. For example, the prediction unit can analyze consumption trends based on sales data for each region. The prediction unit can also perform demand forecasting by taking into account purchasing patterns for each region. For example, the prediction unit can adjust the demand forecast based on purchasing patterns for each region. Furthermore, the prediction unit can predict demand for specific products or services for each region. This allows the prediction unit to improve the accuracy of the demand forecast by taking into account consumption trends for each region. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input sales data and purchasing patterns for each region into AI, which can analyze consumption trends for each region and improve the accuracy of the demand forecast.

[0046] The prediction unit can perform demand prediction taking into account specific events when analyzing past delivery data. The prediction unit, for example, analyzes past delivery data and performs demand prediction taking into account specific events (festivals, sales, etc.). For example, the prediction unit can predict demand during a period when a specific event is held. The prediction unit can also adjust the demand prediction depending on the type of event. For example, the prediction unit can perform demand prediction taking into account the impact of events such as festivals and sales. Furthermore, the prediction unit can propose an optimal delivery route taking into account the impact of events. This allows the prediction unit to perform demand prediction taking into account specific events. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input past delivery data and event information into AI, which can then perform demand prediction taking into account specific events.

[0047] The prediction unit can optimize a delivery route based on current traffic conditions and taking into account a weather forecast. The prediction unit, for example, proposes an optimal delivery route based on current traffic congestion information and a weather forecast. For example, the prediction unit can select an optimal route based on data obtained from real-time traffic sensors and a weather forecast from a weather data provider. The prediction unit can also select a route with a low risk of accidents based on the weather forecast. For example, the prediction unit can propose a route that avoids slippery roads during rainy weather. Furthermore, the prediction unit can monitor the weather forecast in real time and dynamically change the route. This allows the prediction unit to optimize the delivery route by taking into account the weather forecast. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input current traffic conditions and a weather forecast into AI, which then proposes an optimal delivery route.

[0048] When performing demand forecasting, the prediction unit can analyze social media trends to improve the accuracy of the forecast. The prediction unit, for example, analyzes social media trends and reflects the results in the demand forecast. For example, the prediction unit can analyze hashtags and post content and reflect the results in the demand forecast. The prediction unit can also predict demand for specific products and services based on social media trends. For example, the prediction unit can predict demand for products and services that are trending on social media. Furthermore, the prediction unit can propose optimal delivery routes taking social media trends into consideration. This allows the prediction unit to analyze social media trends and improve the accuracy of the demand forecast. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input social media data into AI, which then analyzes trends to perform demand forecasting.

[0049] The driving unit can select a driving route based on vehicle position information, taking into account road congestion. The driving unit, for example, selects a driving route based on vehicle position information, taking into account road congestion. For example, the driving unit can grasp the vehicle's position using GPS data and select a route that avoids congested roads based on data obtained from real-time traffic sensors. The driving unit can also monitor congestion in real time and dynamically change the route. For example, the driving unit can select the shortest route based on the congestion. This allows the driving unit to select a driving route taking into account road congestion. Some or all of the above-mentioned processing in the driving unit may be performed using, for example, AI, or may be performed without using AI. For example, the driving unit can input vehicle position information and data obtained from traffic sensors into AI, which can select an optimal driving route.

[0050] The driving unit can predict signal timing based on traffic conditions and optimize driving. The driving unit, for example, predicts signal timing based on traffic conditions and optimizes driving. For example, the driving unit can predict signal timing based on information from a traffic signal control system to support smooth driving. The driving unit can also analyze past signal patterns and predict signal timing. For example, the driving unit can suggest an optimal speed based on signal timing. Furthermore, the driving unit can monitor signal timing in real time and optimize driving. This allows the driving unit to predict signal timing and optimize driving. Some or all of the above-mentioned processing in the driving unit may be performed using, for example, AI, or may be performed without using AI. For example, the driving unit can input information from traffic conditions and a signal control system to AI, which can predict signal timing and optimize driving.

[0051] The driving unit can apply an algorithm for minimizing fuel consumption when controlling the speed of the vehicle. For example, the driving unit can apply an algorithm for minimizing fuel consumption when controlling the speed of the vehicle. For example, the driving unit can suggest an optimal speed using an eco-driving algorithm. The driving unit can also monitor fuel consumption in real time and adjust the speed. For example, the driving unit can suggest a driving method for minimizing fuel consumption. Furthermore, the driving unit can minimize fuel consumption using an optimal speed control algorithm. This allows the driving unit to apply an algorithm for minimizing fuel consumption. Some or all of the above-mentioned processing in the driving unit may be performed using, for example, AI, or may be performed without using AI. For example, the driving unit can input vehicle speed data to AI, which can calculate a speed for minimizing fuel consumption and optimize driving.

[0052] The driving unit can select a driving route based on the vehicle's position information, taking into account the distance to other vehicles. The driving unit, for example, monitors the distance to other vehicles in real time and selects a safe driving route. For example, the driving unit can measure the distance to other vehicles using LIDAR, radar, or a camera and select a safe driving route. The driving unit can also predict the movement of other vehicles and propose an optimal driving route. For example, the driving unit can select an optimal driving route based on the movement of other vehicles. Furthermore, the driving unit can select the shortest route taking into account the distance to other vehicles. This allows the driving unit to select a driving route taking into account the distance to other vehicles. Some or all of the above-mentioned processing in the driving unit may be performed using, for example, AI, or may be performed without using AI. For example, the driving unit can input distance data to other vehicles into AI, which can select an optimal driving route.

[0053] The driving unit can predict the approach of an emergency vehicle based on traffic conditions and optimize driving. The driving unit, for example, predicts the approach of an emergency vehicle based on traffic conditions and supports safe driving. For example, the driving unit can detect the approach of an emergency vehicle using voice recognition or V2V communication and support safe driving. The driving unit can also suggest an optimal speed in accordance with the approach of the emergency vehicle. For example, the driving unit can monitor the approach of an emergency vehicle in real time and optimize driving. This allows the driving unit to predict the approach of an emergency vehicle and optimize driving. Some or all of the above-mentioned processing in the driving unit may be performed using, for example, AI, or may be performed without using AI. For example, the driving unit can input traffic conditions and approaching emergency vehicle data into AI, which can then suggest an optimal driving method.

[0054] The driving unit can optimize fuel efficiency by taking into account the engine operating state when controlling the vehicle speed. The driving unit, for example, acquires engine speed and load using a sensor and monitors fuel consumption. For example, the driving unit can calculate fuel consumption based on the engine speed and load and optimize fuel efficiency. The driving unit can also analyze the engine operating state in real time and propose an efficient driving method. Furthermore, the driving unit can optimize fuel efficiency by taking into account the engine maintenance state. For example, the driving unit can monitor fuel consumption based on the engine maintenance state and propose an optimal driving method. This allows the driving unit to optimize fuel efficiency by taking into account the engine operating state. Some or all of the above-mentioned processing in the driving unit may be performed using, for example, AI, or may be performed without using AI. For example, the driving unit can input engine operating state data to AI, which can monitor fuel consumption and propose an efficient driving method.

[0055] The data collection unit can calibrate the sensor during data collection to improve data accuracy. The data collection unit, for example, periodically calibrates the sensor to maintain data accuracy. For example, the data collection unit can automate sensor calibration to improve data accuracy in real time. The data collection unit can also correct data based on sensor calibration results. For example, the data collection unit can correct data based on calibration results to improve accuracy. In this way, the data collection unit can calibrate the sensor and improve data accuracy. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input sensor calibration data into AI, which can correct the data.

[0056] The data collection unit can detect outliers during data collection to ensure data quality. The data collection unit, for example, detects outliers in real time during data collection to ensure data quality. For example, the data collection unit can detect outliers using a statistical anomaly detection algorithm. Furthermore, if the data collection unit detects an outlier, it can re-collect data. For example, if the data collection unit detects an outlier, it can correct the data. In this way, the data collection unit can detect outliers and ensure data quality. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input collected data into AI, which can detect outliers and correct the data.

[0057] The data collection unit can integrate data from different sensors during data collection and perform analysis. The data collection unit, for example, integrates and analyzes data from different sensors in real time. For example, the data collection unit can integrate data from different sensors using a data fusion algorithm. The data collection unit can also integrate data from different sensors to improve data accuracy. For example, the data collection unit can integrate data from different sensors using a sensor network. Furthermore, the data collection unit can perform comprehensive analysis based on the data from different sensors. This allows the data collection unit to integrate and analyze data from different sensors. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input data from different sensors into AI, which then integrates the data and performs analysis.

[0058] The data collection unit can detect anomalies by comparing data with past data when collecting data. The data collection unit, for example, compares data with past data in real time to detect anomalies. For example, the data collection unit can detect anomalies based on data from the past year. Furthermore, if the data collection unit detects an anomaly, it can re-collect data. For example, if the data collection unit detects an anomaly, it can correct the data. This allows the data collection unit to detect anomalies by comparing with past data. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input the collected data into AI, which compares the data with past data to detect anomalies and correct the data.

[0059] The safety measure unit can monitor the vehicle's operating state in real time and detect abnormalities during safety measures. The safety measure unit, for example, monitors the vehicle's operating state in real time and detects abnormalities. For example, the safety measure unit can monitor the engine speed, temperature, fuel injection amount, etc. and detect abnormalities. Furthermore, if the safety measure unit detects an abnormality, it can immediately issue a warning. For example, if the safety measure unit detects an abnormality, it can automatically adjust the vehicle's operation. This allows the safety measure unit to monitor the vehicle's operating state in real time and detect abnormalities. Some or all of the above-mentioned processing in the safety measure unit may be performed using AI, for example, or may be performed without using AI. For example, the safety measure unit can input vehicle operating state data to AI, which can detect an abnormality and adjust the vehicle's operation.

[0060] The safety measure unit can avoid collisions by taking into account the distance to other vehicles during safety measures. The safety measure unit, for example, monitors the distance to other vehicles in real time and avoids collisions. For example, the safety measure unit can measure the distance to other vehicles using LIDAR, radar, or a camera and avoid collisions. The safety measure unit can also predict the movement of other vehicles and propose an optimal collision avoidance method. For example, the safety measure unit can select an optimal collision avoidance method based on the movement of other vehicles. Furthermore, the safety measure unit can automatically adjust the operation of the vehicle by taking into account the distance to other vehicles. This allows the safety measure unit to avoid collisions by taking into account the distance to other vehicles. Some or all of the above-described processing in the safety measure unit may be performed using, for example, AI, or may be performed without using AI. For example, the safety measure unit can input distance data to other vehicles into AI, which can then propose an optimal collision avoidance method.

[0061] The safety measure unit can support safe driving by controlling the vehicle speed during safety measures. The safety measure unit, for example, monitors the vehicle speed in real time and supports safe driving. For example, the safety measure unit can automatically adjust the vehicle speed to achieve safe driving. The safety measure unit can also control the vehicle speed and suggest an optimal driving method. For example, the safety measure unit can suggest an optimal driving method based on the legal speed or a speed that maximizes fuel efficiency. In this way, the safety measure unit can support safe driving by controlling the vehicle speed. Some or all of the above-mentioned processing in the safety measure unit may be performed using, for example, AI, or may be performed without using AI. For example, the safety measure unit can input vehicle speed data to AI, which can suggest an optimal driving method.

[0062] The safety measure unit can perform an emergency stop based on the vehicle's location information when taking safety measures. The safety measure unit, for example, monitors the vehicle's location information in real time and performs an emergency stop. For example, the safety measure unit can obtain the vehicle's location information using GPS or RTK-GPS and immediately issue a warning if an emergency stop is necessary. The safety measure unit can also automatically adjust the vehicle's operation if an emergency stop is necessary. This allows the safety measure unit to perform an emergency stop based on the vehicle's location information. Some or all of the above-mentioned processing in the safety measure unit may be performed using, for example, AI, or may be performed without using AI. For example, the safety measure unit can input the vehicle's location information data into AI, which can determine the need for an emergency stop and adjust the vehicle's operation.

[0063] The safety measure unit can propose optimal safety measures based on the vehicle's operating state when implementing safety measures. The safety measure unit, for example, monitors the vehicle's operating state in real time and proposes optimal safety measures. For example, the safety measure unit can monitor the engine's rotation speed, temperature, fuel injection amount, etc. and propose optimal safety measures. The safety measure unit can also determine the priority of safety measures based on the vehicle's operating state. For example, the safety measure unit can implement optimal safety measures taking into account the vehicle's operating state. This allows the safety measure unit to propose optimal safety measures based on the vehicle's operating state. Some or all of the above-mentioned processing in the safety measure unit may be performed using, for example, AI, or may be performed without using AI. For example, the safety measure unit can input vehicle operating state data to AI, which can then propose optimal safety measures.

[0064] The safety measure unit can implement safety measures taking into account the vehicle's maintenance status when implementing safety measures. The safety measure unit, for example, monitors the vehicle's maintenance status in real time and implements safety measures. For example, the safety measure unit can monitor the vehicle's maintenance status based on periodic inspection records and part replacement history and implement safety measures. The safety measure unit can also immediately issue a warning when maintenance is required. For example, the safety measure unit can automatically adjust the vehicle's operation when maintenance is required. This allows the safety measure unit to implement safety measures taking into account the vehicle's maintenance status. Some or all of the above-mentioned processing in the safety measure unit may be performed using, for example, AI, or may be performed without using AI. For example, the safety measure unit can input vehicle maintenance status data into AI, which can then suggest optimal safety measures.

[0065] The providing unit can select optimal information by referring to past provision history when providing information. The providing unit selects optimal information based on, for example, past provision history. For example, the providing unit can analyze the provision history for the past year and provide information tailored to the user's preferences. The providing unit can also provide information at the optimal timing by referring to the past provision history. For example, the providing unit can select optimal information based on the provision history at the time of a specific event. This allows the providing unit to select optimal information by referring to the past provision history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past provision history data into AI, which can select optimal information.

[0066] The providing unit can customize information taking into account the user's current situation when providing the information. The providing unit, for example, monitors the user's current situation in real time and customizes the information. For example, the providing unit can provide optimal information based on the user's current location information and activity status. The providing unit can also determine the priority of information according to the user's current situation. For example, the providing unit can determine the priority of information taking into account the user's current situation and provide optimal information. This allows the providing unit to customize information taking into account the user's current situation. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current situation data into AI, which can customize and provide optimal information.

[0067] When providing information, the providing unit can provide information in an optimal format taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide information tailored to the screen size. For example, the providing unit can provide information optimized for the smartphone's screen size. Furthermore, if the user is using a tablet, the providing unit can provide information optimized for a large screen. For example, the providing unit can provide information tailored to the tablet's screen size. Furthermore, if the user is using a smartwatch, the providing unit can provide concise, highly visible information. This allows the providing unit to provide information in an optimal format taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information into AI, which can then provide the information in an optimal format.

[0068] When providing information, the providing unit can provide information in an optimal format taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide information tailored to the screen size. For example, the providing unit can provide information optimized for the smartphone's screen size. Furthermore, if the user is using a tablet, the providing unit can provide information optimized for a large screen. For example, the providing unit can provide information tailored to the tablet's screen size. Furthermore, if the user is using a smartwatch, the providing unit can provide concise, highly visible information. This allows the providing unit to provide information in an optimal format taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information into AI, which can then provide the information in an optimal format.

[0069] The providing unit can provide information in multiple languages ​​according to the user's language setting when providing the information. The providing unit, for example, automatically sets the language of the information based on the language setting of the user's device. For example, the providing unit can provide information in multiple languages ​​based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. For example, when the user selects a specific language, the providing unit can provide information in that language. This allows the providing unit to provide information in multiple languages ​​according to the user's language setting. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's language setting data into AI, and the AI ​​can provide information in the language most appropriate for the user.

[0070] The providing unit can analyze the user's social media activity and provide related information at the time of providing. The providing unit, for example, analyzes the user's social media activity and provides related information. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. Furthermore, the providing unit can provide information about related places and events by referring to the activity of the user's friends on social media. This allows the providing unit to analyze the user's social media activity and provide related information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media data into AI, which then provides the related information.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The monitoring unit can improve monitoring accuracy by taking geographical obstacles into account when acquiring vehicle position information. For example, the monitoring unit can apply a correction algorithm to improve the accuracy of position information in mountainous areas or areas affected by high-rise buildings. Furthermore, the monitoring unit can supplement the position information using other sensors (e.g., inertial measurement units or dead reckoning) in places where GPS signals are difficult to reach, such as inside tunnels or underground parking lots. Furthermore, the monitoring unit can monitor the effects of geographical obstacles in real time and make corrections as necessary. This allows the monitoring unit to improve monitoring accuracy by taking geographical obstacles into account.

[0073] When making a demand forecast, the forecasting unit can improve the accuracy of the forecast by taking into account a local event calendar. For example, the forecasting unit can collect calendar information about local festivals, sporting events, etc., and reflect this information in the demand forecast. The forecasting unit can also adjust the demand forecast by taking into account the scale of the event and the number of participants. Furthermore, the forecasting unit can propose an optimal delivery route depending on the location and time of the event. This allows the forecasting unit to improve the accuracy of the demand forecast by taking into account a local event calendar.

[0074] The driving unit can provide guidance on eco-driving when controlling the driving of the vehicle. For example, the driving unit can suggest driving methods in real time to maximize fuel efficiency. The driving unit can also adjust the driving style to avoid sudden acceleration and braking. Furthermore, when providing guidance on eco-driving, the driving unit can provide feedback to the driver and support the improvement of driving skills. This allows the driving unit to provide guidance on eco-driving and improve fuel efficiency.

[0075] When monitoring the vehicle's driving status, the monitoring unit can perform analysis taking into account road congestion. For example, the monitoring unit can evaluate the road congestion status based on data obtained from real-time traffic sensors. The monitoring unit can also suggest driving routes that avoid congested roads. Furthermore, the monitoring unit can adjust the driving speed according to the congestion status to support safe driving. This allows the monitoring unit to analyze the driving status taking into account road congestion.

[0076] When making a demand forecast, the prediction unit can improve the accuracy of the forecast by taking into account the user's purchase history. For example, the prediction unit can analyze the user's past purchase history and reflect it in the demand forecast. The prediction unit can also adjust the demand forecast by taking into account the user's purchasing patterns. Furthermore, the prediction unit can analyze the purchasing trends of a specific user group and improve the accuracy of the demand forecast. In this way, the prediction unit can improve the accuracy of the demand forecast by taking into account the user's purchase history.

[0077] The processing flow of the first embodiment will be briefly explained below.

[0078] Step 1: The monitoring unit monitors the vehicle's location, driving conditions, traffic congestion information, and fuel consumption. For example, the monitoring unit analyzes information obtained from cameras and sensors installed in the vehicle to grasp the vehicle's status in real time. The monitoring unit can also obtain vehicle location information from GPS data and analyze driving conditions from camera footage. Furthermore, the monitoring unit can obtain fuel consumption data from sensors to support efficient driving. Step 2: The prediction unit performs demand prediction and delivery route optimization processing based on the information monitored by the monitoring unit. For example, the prediction unit performs demand prediction based on past delivery data and current traffic conditions, and calculates the optimal delivery route. The prediction unit can also perform demand prediction using statistical models and machine learning algorithms. Step 3: The driving unit automatically drives the truck based on the results obtained by the prediction unit. For example, the driving unit selects the optimal speed and route based on the vehicle's location information and traffic conditions, and drives safely. The driving unit can use autonomous driving technology to control the truck's driving and drive it to its destination.

[0079] (Example 2) A system according to an embodiment of the present invention uses AI to automatically drive and remotely monitor trucks. This system monitors vehicle location, driving conditions, traffic congestion information, and fuel consumption, and then performs demand forecasting and optimizes delivery routes to automatically drive trucks. This system can solve the Logistics 2024 problem of driver shortages. For example, the system uses AI to analyze information obtained from cameras and sensors mounted on vehicles and grasp the vehicle's status in real time. Next, the AI ​​predicts demand based on past delivery data and current traffic conditions and calculates the optimal delivery route. Furthermore, the AI ​​controls the truck's driving and drives it to its destination. This allows the system to achieve efficient logistics and reduce the burden on drivers.

[0080] An autonomous driving system according to an embodiment includes a monitoring unit, a prediction unit, and a driving unit. The monitoring unit monitors the vehicle's location, driving conditions, traffic congestion information, and fuel consumption. For example, the monitoring unit analyzes information obtained from cameras and sensors mounted on the vehicle to grasp the vehicle's status in real time. The monitoring unit can also acquire vehicle location information from GPS data and analyze driving conditions from camera footage. The monitoring unit can also acquire fuel consumption from sensors to support efficient driving. The prediction unit performs demand forecasting and delivery route optimization processing based on the information monitored by the monitoring unit. For example, the prediction unit performs demand forecasting based on past delivery data and current traffic conditions and calculates an optimal delivery route. The prediction unit can also perform demand forecasting using statistical models and machine learning algorithms. The driving unit automatically drives the truck based on the results obtained by the prediction unit. For example, the driving unit selects an optimal speed and route based on the vehicle's location information and traffic conditions to drive safely. The driving unit can control the truck's driving using autonomous driving technology and drive it to its destination. As a result, the autonomous driving system according to the embodiment can monitor vehicle location, driving conditions, traffic congestion information, and fuel consumption, perform demand forecasts, optimize delivery routes, and drive trucks automatically.

[0081] The monitoring unit analyzes information obtained from cameras and sensors mounted on the vehicle and can grasp the vehicle's status in real time. The monitoring unit, for example, analyzes video data obtained from cameras mounted on the vehicle to grasp the vehicle's driving status. The monitoring unit can also analyze data obtained from sensors to obtain fuel consumption and vehicle location information. Furthermore, the monitoring unit can grasp the vehicle's location in real time using GPS data. This allows the monitoring unit to grasp the vehicle's status in real time. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data obtained from cameras and sensors into AI, which can analyze the data to grasp the vehicle's status.

[0082] The prediction unit can perform demand prediction based on past delivery data and current traffic conditions and calculate an optimized delivery route. The prediction unit, for example, analyzes past delivery data to perform demand prediction. For example, the prediction unit can analyze demand trends based on delivery data from the past year. The prediction unit can also calculate an optimal delivery route based on current traffic conditions. For example, the prediction unit can select an optimal route based on data obtained from real-time traffic sensors. Furthermore, the prediction unit can perform demand prediction using statistical models and machine learning algorithms and calculate an optimal delivery route. This allows the prediction unit to perform demand prediction and calculate an optimal delivery route. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input past delivery data and current traffic conditions into AI, which can then perform demand prediction and calculate an optimal delivery route.

[0083] The driving unit can select an optimized speed and route based on the vehicle's location information and traffic conditions, and drive safely. The driving unit, for example, selects an optimal speed based on the vehicle's location information. For example, the driving unit can grasp the vehicle's location using GPS data and calculate an optimal speed. The driving unit can also select an optimal route based on traffic conditions. For example, the driving unit can select an optimal route based on data obtained from real-time traffic sensors. Furthermore, the driving unit can control the driving of the truck using autonomous driving technology and drive safely. This allows the driving unit to select an optimal speed and route and drive safely. Some or all of the above-mentioned processing in the driving unit may be performed using, for example, AI, or may be performed without using AI. For example, the driving unit inputs the vehicle's location information and traffic conditions into AI, which then calculates the optimal speed and route, and drives safely.

[0084] The monitoring unit can acquire fuel consumption data from a sensor and support efficient driving. The monitoring unit, for example, analyzes data obtained from a fuel sensor to determine fuel consumption. For example, the monitoring unit can calculate fuel consumption based on data obtained from the vehicle's on-board computer. The monitoring unit can also monitor fuel consumption in real time and support efficient driving. For example, the monitoring unit can suggest a driving method that maximizes fuel efficiency. Furthermore, the monitoring unit can suggest a driving method that minimizes fuel consumption using eco-driving technology. In this way, the monitoring unit can acquire fuel consumption data and support efficient driving. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data obtained from the fuel sensor into AI, which can calculate fuel consumption and suggest an efficient driving method.

[0085] The system includes a data collection unit that collects data to be used for demand forecasting and delivery route optimization. The data collection unit, for example, collects data from sensors and stores it in a cloud database. For example, the data collection unit can collect data obtained from sensors mounted on vehicles in real time and store it in a cloud database. The data collection unit also collects past delivery data and uses it for demand forecasting and delivery route optimization. For example, the data collection unit can collect delivery data from the past year and use it for demand forecasting. Furthermore, the data collection unit can collect data obtained from real-time traffic sensors and use it for delivery route optimization. In this way, the data collection unit can collect data to be used for demand forecasting and delivery route optimization. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit inputs data obtained from sensors into AI, which analyzes the data and uses it for demand forecasting and delivery route optimization.

[0086] The system includes a safety measure unit that describes specific details of the autonomous driving technology and safety measures. The safety measure unit, for example, describes the specific details of the autonomous driving technology. For example, the safety measure unit may describe the use of LIDAR, cameras, radar, etc. The safety measure unit may also describe specific details of the safety measures. For example, the safety measure unit may describe the use of a collision avoidance system or an emergency braking system. The safety measure unit may also describe standards for the autonomous driving technology. For example, the safety measure unit may describe standards for level 3 autonomous driving and level 4 autonomous driving. This allows the safety measure unit to describe the specific details of the autonomous driving technology and safety measures. Some or all of the above-described processing in the safety measure unit may be performed using, for example, AI, or may be performed without using AI. For example, the safety measure unit may input the details of the autonomous driving technology and safety measures into AI, which may analyze the information and propose optimal safety measures.

[0087] The system includes a providing unit that provides information to clarify the background and issues of the Logistics 2024 Problem. The providing unit provides information to clarify the background and issues of the Logistics 2024 Problem, for example. For example, the providing unit can provide background information such as labor shortages and stricter environmental regulations. The providing unit can also provide specific issues of the Logistics 2024 Problem. For example, the providing unit can provide issues such as driver shortages and rising logistics costs. The providing unit can also provide solutions to the Logistics 2024 Problem. For example, the providing unit can propose the introduction of autonomous driving technology or remote monitoring technology using AI. This allows the providing unit to provide information to clarify the background and issues of the Logistics 2024 Problem. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input information about the background and issues of the Logistics 2024 Problem into AI, which can then analyze the information and provide optimal information.

[0088] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user emotions. For example, the monitoring unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the monitoring unit calculates an emotion score based on changes in facial expressions and adjusts the monitoring frequency. The monitoring unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the monitoring unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the monitoring frequency. The monitoring unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the monitoring unit calculates an emotion score based on heart rate fluctuations and adjusts the monitoring frequency. This allows the monitoring unit to adjust the monitoring frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may input image data of a user taken with a camera into the generation AI, which may then estimate the user's emotions and adjust the monitoring frequency.

[0089] The monitoring unit can improve monitoring accuracy by taking weather information into account when acquiring vehicle position information. The monitoring unit, for example, acquires weather information from a weather data provider and corrects the vehicle position information. For example, the monitoring unit can apply a correction algorithm because the accuracy of GPS signals decreases during rainy weather. The monitoring unit can also correct the position information by taking into account the slipperiness of roads on snowy days. Furthermore, the monitoring unit can acquire highly accurate position information using normal GPS signals during sunny weather. This allows the monitoring unit to improve monitoring accuracy by taking weather information into account. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input weather information into AI, which then corrects the position information to improve monitoring accuracy.

[0090] When monitoring the driving conditions, the monitoring unit can perform analysis taking into account the road conditions. The monitoring unit, for example, detects the road pavement conditions using a sensor and analyzes the driving conditions. For example, the monitoring unit can analyze vibration data and evaluate road unevenness and pavement conditions. The monitoring unit can also analyze the driving conditions taking into account the road gradient. For example, on a road with a steep gradient, the monitoring unit can analyze the driving conditions taking into account the engine load. Furthermore, when the road conditions are good, the monitoring unit can perform analysis based on normal driving data. This allows the monitoring unit to analyze the driving conditions taking into account the road conditions. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input road condition data into AI, which can then analyze the driving conditions.

[0091] The monitoring unit can support efficient driving by taking into account the vehicle load when monitoring fuel consumption. The monitoring unit, for example, detects the vehicle load using a sensor and monitors fuel consumption. For example, the monitoring unit can suggest an efficient driving method because fuel consumption increases when the load is heavy. Furthermore, the monitoring unit can suggest a driving method that minimizes fuel consumption when the load is light. Furthermore, the monitoring unit can monitor fuel consumption in real time according to fluctuations in the load and suggest an optimal driving method. This allows the monitoring unit to support efficient driving by taking into account the vehicle load. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input load data to AI, which monitors fuel consumption and suggests an efficient driving method.

[0092] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. For example, the monitoring unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the monitoring unit calculates an emotion score based on changes in facial expressions and adjusts the display method of the monitoring results. The monitoring unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the monitoring unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the display method of the monitoring results. The monitoring unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the monitoring unit calculates an emotion score based on heart rate fluctuations and adjusts the display method of the monitoring results. This allows the monitoring unit to adjust the display method of the monitoring results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may input image data of a user taken with a camera to a generation AI, which may estimate the user's emotions and adjust the display method of the monitoring results.

[0093] When acquiring vehicle position information, the monitoring unit can determine the relative position by taking into account the position information of other vehicles. For example, the monitoring unit acquires position information of other vehicles in real time and determines the relative position. For example, the monitoring unit can acquire position information of other vehicles using V2V communication and determine the relative position. The monitoring unit can also acquire position information of other vehicles based on information from a traffic data provider and determine the relative position. Furthermore, the monitoring unit can calculate the distance to other vehicles and support safe driving. For example, the monitoring unit can monitor the distance to other vehicles in real time and select a safe driving route. This allows the monitoring unit to determine the relative position by taking into account the position information of other vehicles. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input position information of other vehicles into AI, which can determine the relative position.

[0094] When monitoring the driving conditions, the monitoring unit can analyze vehicle vibration data to evaluate the road condition. The monitoring unit, for example, acquires vehicle vibration data using a sensor and evaluates the road condition. For example, the monitoring unit can analyze the vibration data to evaluate road unevenness and pavement condition. The monitoring unit can also evaluate the slipperiness of the road from the vibration data to support safe driving. Furthermore, the monitoring unit can identify areas requiring road maintenance based on the vibration data. For example, the monitoring unit can analyze the vibration data to identify areas requiring road maintenance. This allows the monitoring unit to analyze the vehicle vibration data and evaluate the road condition. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the vibration data to AI, which can evaluate the road condition.

[0095] When monitoring fuel consumption, the monitoring unit can optimize fuel efficiency by taking into account the engine operating state. The monitoring unit, for example, acquires engine rotation speed and load using a sensor and monitors fuel consumption. For example, the monitoring unit can calculate fuel consumption based on the engine rotation speed and load and optimize fuel efficiency. The monitoring unit can also analyze the engine operating state in real time and suggest an efficient driving method. Furthermore, the monitoring unit can optimize fuel efficiency by taking into account the engine maintenance state. For example, the monitoring unit can monitor fuel consumption based on the engine maintenance state and suggest an optimal driving method. This allows the monitoring unit to optimize fuel efficiency by taking into account the engine operating state. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input engine operating state data to AI, which can monitor fuel consumption and suggest an efficient driving method.

[0096] The prediction unit can estimate a user's emotion and adjust the accuracy of the demand forecast based on the estimated user emotion. For example, the prediction unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the prediction unit calculates an emotion score based on changes in facial expression and adjusts the accuracy of the demand forecast. The prediction unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the prediction unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the accuracy of the demand forecast. The prediction unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the prediction unit calculates an emotion score based on heart rate fluctuations and adjusts the accuracy of the demand forecast. This allows the prediction unit to adjust the accuracy of the demand forecast based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit may input image data of a user taken with a camera to the generation AI, which may then estimate the user's emotions and adjust the accuracy of the demand forecast.

[0097] The prediction unit can perform demand forecasting taking seasonal fluctuations into account when analyzing past delivery data. The prediction unit, for example, analyzes past delivery data and performs demand forecasting taking seasonal demand fluctuations into account. For example, the prediction unit can analyze seasonal demand patterns based on delivery data from the past year. The prediction unit can also perform demand forecasting taking into account the effects of temperature and weather. For example, the prediction unit can adjust the demand forecast based on temperature and weather data. Furthermore, the prediction unit can predict demand for specific products and services by season. This allows the prediction unit to perform demand forecasting taking seasonal fluctuations into account. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input past delivery data, temperature, and weather data into AI, which can then perform demand forecasting taking seasonal fluctuations into account.

[0098] The prediction unit can predict the risk of an accident based on current traffic conditions and optimize the delivery route. The prediction unit can predict the risk of an accident based on, for example, current traffic congestion information. For example, the prediction unit can evaluate the risk of an accident based on data obtained from real-time traffic sensors. The prediction unit can also predict the risk of an accident based on past accident data. For example, the prediction unit can analyze past accident data and predict the risk of an accident by comparing it with current traffic conditions. Furthermore, the prediction unit can propose an optimal delivery route that avoids routes with a high risk of accidents. This allows the prediction unit to predict the risk of an accident and optimize the delivery route. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input current traffic conditions and past accident data into AI, which can predict the risk of an accident and propose an optimal delivery route.

[0099] When performing demand forecasting, the prediction unit can improve the accuracy of the forecast by taking into account consumption trends for each region. The prediction unit, for example, analyzes consumption trends for each region and reflects the results in the demand forecast. For example, the prediction unit can analyze consumption trends based on sales data for each region. The prediction unit can also perform demand forecasting by taking into account purchasing patterns for each region. For example, the prediction unit can adjust the demand forecast based on purchasing patterns for each region. Furthermore, the prediction unit can predict demand for specific products or services for each region. This allows the prediction unit to improve the accuracy of the demand forecast by taking into account consumption trends for each region. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input sales data and purchasing patterns for each region into AI, which can analyze consumption trends for each region and improve the accuracy of the demand forecast.

[0100] The prediction unit can estimate the user's emotion and adjust the display method of the prediction result based on the estimated user emotion. For example, the prediction unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the prediction unit calculates an emotion score based on changes in facial expression and adjusts the display method of the prediction result. The prediction unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the prediction unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the display method of the prediction result. The prediction unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the prediction unit calculates an emotion score based on heart rate fluctuations and adjusts the display method of the prediction result. This allows the prediction unit to adjust the display method of the prediction result based on the user's emotion. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit may input image data of a user taken with a camera to the generation AI, which may estimate the emotion and adjust the display method of the prediction result.

[0101] The prediction unit can perform demand prediction taking into account specific events when analyzing past delivery data. The prediction unit, for example, analyzes past delivery data and performs demand prediction taking into account specific events (festivals, sales, etc.). For example, the prediction unit can predict demand during a period when a specific event is held. The prediction unit can also adjust the demand prediction depending on the type of event. For example, the prediction unit can perform demand prediction taking into account the impact of events such as festivals and sales. Furthermore, the prediction unit can propose an optimal delivery route taking into account the impact of events. This allows the prediction unit to perform demand prediction taking into account specific events. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input past delivery data and event information into AI, which can then perform demand prediction taking into account specific events.

[0102] The prediction unit can optimize a delivery route based on current traffic conditions and taking into account a weather forecast. The prediction unit, for example, proposes an optimal delivery route based on current traffic congestion information and a weather forecast. For example, the prediction unit can select an optimal route based on data obtained from real-time traffic sensors and a weather forecast from a weather data provider. The prediction unit can also select a route with a low risk of accidents based on the weather forecast. For example, the prediction unit can propose a route that avoids slippery roads during rainy weather. Furthermore, the prediction unit can monitor the weather forecast in real time and dynamically change the route. This allows the prediction unit to optimize the delivery route by taking into account the weather forecast. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input current traffic conditions and a weather forecast into AI, which then proposes an optimal delivery route.

[0103] When performing demand forecasting, the prediction unit can analyze social media trends to improve the accuracy of the forecast. The prediction unit, for example, analyzes social media trends and reflects the results in the demand forecast. For example, the prediction unit can analyze hashtags and post content and reflect the results in the demand forecast. The prediction unit can also predict demand for specific products and services based on social media trends. For example, the prediction unit can predict demand for products and services that are trending on social media. Furthermore, the prediction unit can propose optimal delivery routes taking social media trends into consideration. This allows the prediction unit to analyze social media trends and improve the accuracy of the demand forecast. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input social media data into AI, which then analyzes trends to perform demand forecasting.

[0104] The driving unit can estimate the user's emotions and adjust the driving style based on the estimated user emotions. For example, the driving unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the driving unit calculates an emotion score based on changes in facial expressions and adjusts the driving style. The driving unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the driving unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the driving style. The driving unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the driving unit calculates an emotion score based on heart rate fluctuations and adjusts the driving style. This allows the driving unit to adjust the driving style based on the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the driving unit may be performed using, for example, AI, or may be performed without using AI. For example, the driving unit may input image data of the user taken by a camera into the generation AI, which may then estimate the user's emotions and adjust the driving style.

[0105] The driving unit can select a driving route based on vehicle position information, taking into account road congestion. The driving unit, for example, selects a driving route based on vehicle position information, taking into account road congestion. For example, the driving unit can grasp the vehicle's position using GPS data and select a route that avoids congested roads based on data obtained from real-time traffic sensors. The driving unit can also monitor congestion in real time and dynamically change the route. For example, the driving unit can select the shortest route based on the congestion. This allows the driving unit to select a driving route taking into account road congestion. Some or all of the above-mentioned processing in the driving unit may be performed using, for example, AI, or may be performed without using AI. For example, the driving unit can input vehicle position information and data obtained from traffic sensors into AI, which can select an optimal driving route.

[0106] The driving unit can predict signal timing based on traffic conditions and optimize driving. The driving unit, for example, predicts signal timing based on traffic conditions and optimizes driving. For example, the driving unit can predict signal timing based on information from a traffic signal control system to support smooth driving. The driving unit can also analyze past signal patterns and predict signal timing. For example, the driving unit can suggest an optimal speed based on signal timing. Furthermore, the driving unit can monitor signal timing in real time and optimize driving. This allows the driving unit to predict signal timing and optimize driving. Some or all of the above-mentioned processing in the driving unit may be performed using, for example, AI, or may be performed without using AI. For example, the driving unit can input information from traffic conditions and a signal control system to AI, which can predict signal timing and optimize driving.

[0107] The driving unit can apply an algorithm for minimizing fuel consumption when controlling the speed of the vehicle. For example, the driving unit can apply an algorithm for minimizing fuel consumption when controlling the speed of the vehicle. For example, the driving unit can suggest an optimal speed using an eco-driving algorithm. The driving unit can also monitor fuel consumption in real time and adjust the speed. For example, the driving unit can suggest a driving method for minimizing fuel consumption. Furthermore, the driving unit can minimize fuel consumption using an optimal speed control algorithm. This allows the driving unit to apply an algorithm for minimizing fuel consumption. Some or all of the above-mentioned processing in the driving unit may be performed using, for example, AI, or may be performed without using AI. For example, the driving unit can input vehicle speed data to AI, which can calculate a speed for minimizing fuel consumption and optimize driving.

[0108] The driving unit can estimate the user's emotions and adjust the driving speed based on the estimated user emotions. For example, the driving unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the driving unit calculates an emotion score based on changes in facial expressions and adjusts the driving speed. The driving unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the driving unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the driving speed. The driving unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the driving unit calculates an emotion score based on heart rate fluctuations and adjusts the driving speed. This allows the driving unit to adjust the driving speed based on the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generative AI. Some or all of the above-described processing in the driving unit may be performed using, for example, AI, or may be performed without using AI. For example, the driving unit may input image data of the user taken by a camera to the generation AI, which may then estimate the user's emotions and adjust the driving speed.

[0109] The driving unit can select a driving route based on the vehicle's position information, taking into account the distance to other vehicles. The driving unit, for example, monitors the distance to other vehicles in real time and selects a safe driving route. For example, the driving unit can measure the distance to other vehicles using LIDAR, radar, or a camera and select a safe driving route. The driving unit can also predict the movement of other vehicles and propose an optimal driving route. For example, the driving unit can select an optimal driving route based on the movement of other vehicles. Furthermore, the driving unit can select the shortest route taking into account the distance to other vehicles. This allows the driving unit to select a driving route taking into account the distance to other vehicles. Some or all of the above-mentioned processing in the driving unit may be performed using, for example, AI, or may be performed without using AI. For example, the driving unit can input distance data to other vehicles into AI, which can select an optimal driving route.

[0110] The driving unit can predict the approach of an emergency vehicle based on traffic conditions and optimize driving. The driving unit, for example, predicts the approach of an emergency vehicle based on traffic conditions and supports safe driving. For example, the driving unit can detect the approach of an emergency vehicle using voice recognition or V2V communication and support safe driving. The driving unit can also suggest an optimal speed in accordance with the approach of the emergency vehicle. For example, the driving unit can monitor the approach of an emergency vehicle in real time and optimize driving. This allows the driving unit to predict the approach of an emergency vehicle and optimize driving. Some or all of the above-mentioned processing in the driving unit may be performed using, for example, AI, or may be performed without using AI. For example, the driving unit can input traffic conditions and approaching emergency vehicle data into AI, which can then suggest an optimal driving method.

[0111] The driving unit can optimize fuel efficiency by taking into account the engine operating state when controlling the vehicle speed. The driving unit, for example, acquires engine speed and load using a sensor and monitors fuel consumption. For example, the driving unit can calculate fuel consumption based on the engine speed and load and optimize fuel efficiency. The driving unit can also analyze the engine operating state in real time and propose an efficient driving method. Furthermore, the driving unit can optimize fuel efficiency by taking into account the engine maintenance state. For example, the driving unit can monitor fuel consumption based on the engine maintenance state and propose an optimal driving method. This allows the driving unit to optimize fuel efficiency by taking into account the engine operating state. Some or all of the above-mentioned processing in the driving unit may be performed using, for example, AI, or may be performed without using AI. For example, the driving unit can input engine operating state data to AI, which can monitor fuel consumption and propose an efficient driving method.

[0112] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated user emotions. For example, the data collection unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the data collection unit calculates an emotion score based on changes in facial expressions and adjusts the frequency of data collection. The data collection unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the data collection unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the frequency of data collection. The data collection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the data collection unit calculates an emotion score based on fluctuations in heart rate and adjusts the frequency of data collection. This allows the data collection unit to adjust the frequency of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, or may be performed without using AI. For example, the data collection unit may input image data of a user taken with a camera into the generation AI, which may then estimate emotions and adjust the frequency of data collection.

[0113] The data collection unit can calibrate the sensor during data collection to improve data accuracy. The data collection unit, for example, periodically calibrates the sensor to maintain data accuracy. For example, the data collection unit can automate sensor calibration to improve data accuracy in real time. The data collection unit can also correct data based on sensor calibration results. For example, the data collection unit can correct data based on calibration results to improve accuracy. In this way, the data collection unit can calibrate the sensor and improve data accuracy. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input sensor calibration data into AI, which can correct the data.

[0114] The data collection unit can detect outliers during data collection to ensure data quality. The data collection unit, for example, detects outliers in real time during data collection to ensure data quality. For example, the data collection unit can detect outliers using a statistical anomaly detection algorithm. Furthermore, if the data collection unit detects an outlier, it can re-collect data. For example, if the data collection unit detects an outlier, it can correct the data. In this way, the data collection unit can detect outliers and ensure data quality. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input collected data into AI, which can detect outliers and correct the data.

[0115] The data collection unit can estimate the user's emotions and prioritize the collected data based on the estimated user emotions. For example, the data collection unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the data collection unit calculates an emotion score based on changes in facial expressions and prioritizes the collected data. The data collection unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the data collection unit analyzes the tone and speed of the voice, calculates an emotion score, and prioritizes the collected data. The data collection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the data collection unit calculates an emotion score based on heart rate fluctuations and prioritizes the collected data. This allows the data collection unit to prioritize the collected data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit may input image data of a user taken with a camera to a generation AI, which may then estimate emotions and determine the priority of the collected data.

[0116] The data collection unit can integrate data from different sensors during data collection and perform analysis. The data collection unit, for example, integrates and analyzes data from different sensors in real time. For example, the data collection unit can integrate data from different sensors using a data fusion algorithm. The data collection unit can also integrate data from different sensors to improve data accuracy. For example, the data collection unit can integrate data from different sensors using a sensor network. Furthermore, the data collection unit can perform comprehensive analysis based on the data from different sensors. This allows the data collection unit to integrate and analyze data from different sensors. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input data from different sensors into AI, which then integrates the data and performs analysis.

[0117] The data collection unit can detect anomalies by comparing data with past data when collecting data. The data collection unit, for example, compares data with past data in real time to detect anomalies. For example, the data collection unit can detect anomalies based on data from the past year. Furthermore, if the data collection unit detects an anomaly, it can re-collect data. For example, if the data collection unit detects an anomaly, it can correct the data. This allows the data collection unit to detect anomalies by comparing with past data. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input the collected data into AI, which compares the data with past data to detect anomalies and correct the data.

[0118] The safety measure unit can estimate the user's emotions and adjust the strength of safety measures based on the estimated user emotions. For example, the safety measure unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the safety measure unit calculates an emotion score based on changes in facial expressions and adjusts the strength of the safety measures. The safety measure unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the safety measure unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the strength of the safety measures. The safety measure unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the safety measure unit calculates an emotion score based on fluctuations in heart rate and adjusts the strength of the safety measures. This allows the safety measure unit to adjust the strength of the safety measures based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the safety measure unit may be performed using AI, for example, or may be performed without using AI. For example, the safety measure unit inputs image data of a user captured by a camera into the generation AI, which can estimate emotions and adjust the strength of the safety measures.

[0119] The safety measure unit can monitor the vehicle's operating state in real time and detect abnormalities during safety measures. The safety measure unit, for example, monitors the vehicle's operating state in real time and detects abnormalities. For example, the safety measure unit can monitor the engine speed, temperature, fuel injection amount, etc. and detect abnormalities. Furthermore, if the safety measure unit detects an abnormality, it can immediately issue a warning. For example, if the safety measure unit detects an abnormality, it can automatically adjust the vehicle's operation. This allows the safety measure unit to monitor the vehicle's operating state in real time and detect abnormalities. Some or all of the above-mentioned processing in the safety measure unit may be performed using AI, for example, or may be performed without using AI. For example, the safety measure unit can input vehicle operating state data to AI, which can detect an abnormality and adjust the vehicle's operation.

[0120] The safety measure unit can avoid collisions by taking into account the distance to other vehicles during safety measures. The safety measure unit, for example, monitors the distance to other vehicles in real time and avoids collisions. For example, the safety measure unit can measure the distance to other vehicles using LIDAR, radar, or a camera and avoid collisions. The safety measure unit can also predict the movement of other vehicles and propose an optimal collision avoidance method. For example, the safety measure unit can select an optimal collision avoidance method based on the movement of other vehicles. Furthermore, the safety measure unit can automatically adjust the operation of the vehicle by taking into account the distance to other vehicles. This allows the safety measure unit to avoid collisions by taking into account the distance to other vehicles. Some or all of the above-described processing in the safety measure unit may be performed using, for example, AI, or may be performed without using AI. For example, the safety measure unit can input distance data to other vehicles into AI, which can then propose an optimal collision avoidance method.

[0121] The safety measure unit can support safe driving by controlling the vehicle speed during safety measures. The safety measure unit, for example, monitors the vehicle speed in real time and supports safe driving. For example, the safety measure unit can automatically adjust the vehicle speed to achieve safe driving. The safety measure unit can also control the vehicle speed and suggest an optimal driving method. For example, the safety measure unit can suggest an optimal driving method based on the legal speed or a speed that maximizes fuel efficiency. In this way, the safety measure unit can support safe driving by controlling the vehicle speed. Some or all of the above-mentioned processing in the safety measure unit may be performed using, for example, AI, or may be performed without using AI. For example, the safety measure unit can input vehicle speed data to AI, which can suggest an optimal driving method.

[0122] The safety measure unit can estimate the user's emotions and determine the priority of safety measures based on the estimated user emotions. For example, the safety measure unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the safety measure unit calculates an emotion score based on changes in facial expressions and determines the priority of safety measures. The safety measure unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the safety measure unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the priority of safety measures. The safety measure unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the safety measure unit calculates an emotion score based on fluctuations in heart rate and determines the priority of safety measures. This allows the safety measure unit to determine the priority of safety measures based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the safety measure unit may be performed using AI, for example, or may be performed without using AI. For example, the safety measure unit may input image data of a user captured by a camera into the generation AI, which may then estimate emotions and determine the priority of safety measures.

[0123] The safety measure unit can perform an emergency stop based on the vehicle's location information when taking safety measures. The safety measure unit, for example, monitors the vehicle's location information in real time and performs an emergency stop. For example, the safety measure unit can obtain the vehicle's location information using GPS or RTK-GPS and immediately issue a warning if an emergency stop is necessary. The safety measure unit can also automatically adjust the vehicle's operation if an emergency stop is necessary. This allows the safety measure unit to perform an emergency stop based on the vehicle's location information. Some or all of the above-mentioned processing in the safety measure unit may be performed using, for example, AI, or may be performed without using AI. For example, the safety measure unit can input the vehicle's location information data into AI, which can determine the need for an emergency stop and adjust the vehicle's operation.

[0124] The safety measure unit can propose optimal safety measures based on the vehicle's operating state when implementing safety measures. The safety measure unit, for example, monitors the vehicle's operating state in real time and proposes optimal safety measures. For example, the safety measure unit can monitor the engine's rotation speed, temperature, fuel injection amount, etc. and propose optimal safety measures. The safety measure unit can also determine the priority of safety measures based on the vehicle's operating state. For example, the safety measure unit can implement optimal safety measures taking into account the vehicle's operating state. This allows the safety measure unit to propose optimal safety measures based on the vehicle's operating state. Some or all of the above-mentioned processing in the safety measure unit may be performed using, for example, AI, or may be performed without using AI. For example, the safety measure unit can input vehicle operating state data to AI, which can then propose optimal safety measures.

[0125] The safety measure unit can implement safety measures taking into account the vehicle's maintenance status when implementing safety measures. The safety measure unit, for example, monitors the vehicle's maintenance status in real time and implements safety measures. For example, the safety measure unit can monitor the vehicle's maintenance status based on periodic inspection records and part replacement history and implement safety measures. The safety measure unit can also immediately issue a warning when maintenance is required. For example, the safety measure unit can automatically adjust the vehicle's operation when maintenance is required. This allows the safety measure unit to implement safety measures taking into account the vehicle's maintenance status. Some or all of the above-mentioned processing in the safety measure unit may be performed using, for example, AI, or may be performed without using AI. For example, the safety measure unit can input vehicle maintenance status data into AI, which can then suggest optimal safety measures.

[0126] The providing unit can estimate the user's emotion and adjust the content of the information to be provided based on the estimated user's emotion. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expression and adjusts the content of the information to be provided. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the content of the information to be provided. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on heart rate fluctuations and adjusts the content of the information to be provided. This allows the providing unit to adjust the content of the information to be provided based on the user's emotion. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input image data of a user taken with a camera to the generating AI, which may estimate the user's emotions and adjust the content of the information to be provided.

[0127] The providing unit can select optimal information by referring to past provision history when providing information. The providing unit selects optimal information based on, for example, past provision history. For example, the providing unit can analyze the provision history for the past year and provide information tailored to the user's preferences. The providing unit can also provide information at the optimal timing by referring to the past provision history. For example, the providing unit can select optimal information based on the provision history at the time of a specific event. This allows the providing unit to select optimal information by referring to the past provision history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past provision history data into AI, which can select optimal information.

[0128] The providing unit can customize information taking into account the user's current situation when providing the information. The providing unit, for example, monitors the user's current situation in real time and customizes the information. For example, the providing unit can provide optimal information based on the user's current location information and activity status. The providing unit can also determine the priority of information according to the user's current situation. For example, the providing unit can determine the priority of information taking into account the user's current situation and provide optimal information. This allows the providing unit to customize information taking into account the user's current situation. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current situation data into AI, which can customize and provide optimal information.

[0129] When providing information, the providing unit can provide information in an optimal format taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide information tailored to the screen size. For example, the providing unit can provide information optimized for the smartphone's screen size. Furthermore, if the user is using a tablet, the providing unit can provide information optimized for a large screen. For example, the providing unit can provide information tailored to the tablet's screen size. Furthermore, if the user is using a smartwatch, the providing unit can provide concise, highly visible information. This allows the providing unit to provide information in an optimal format taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information into AI, which can then provide the information in an optimal format.

[0130] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user emotions. For example, the providing unit captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expression and determines the priority of information to be provided. The providing unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the priority of information to be provided. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on heart rate fluctuations and determines the priority of information to be provided. This allows the providing unit to determine the priority of information to be provided based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input image data of a user taken with a camera to the generating AI, which may infer emotions and determine the priority of information to be provided.

[0131] When providing information, the providing unit can provide information in an optimal format taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide information tailored to the screen size. For example, the providing unit can provide information optimized for the smartphone's screen size. Furthermore, if the user is using a tablet, the providing unit can provide information optimized for a large screen. For example, the providing unit can provide information tailored to the tablet's screen size. Furthermore, if the user is using a smartwatch, the providing unit can provide concise, highly visible information. This allows the providing unit to provide information in an optimal format taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information into AI, which can then provide the information in an optimal format.

[0132] The providing unit can provide information in multiple languages ​​according to the user's language setting when providing the information. The providing unit, for example, automatically sets the language of the information based on the language setting of the user's device. For example, the providing unit can provide information in multiple languages ​​based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. For example, when the user selects a specific language, the providing unit can provide information in that language. This allows the providing unit to provide information in multiple languages ​​according to the user's language setting. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's language setting data into AI, and the AI ​​can provide information in the language most appropriate for the user.

[0133] The providing unit can analyze the user's social media activity and provide related information at the time of providing. The providing unit, for example, analyzes the user's social media activity and provides related information. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. Furthermore, the providing unit can provide information about related places and events by referring to the activity of the user's friends on social media. This allows the providing unit to analyze the user's social media activity and provide related information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media data into AI, which then provides the related information. === Hard Collateral 1-1 === Each of the multiple elements, including the monitoring unit, prediction unit, driving unit, data collection unit, safety unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the monitoring unit monitors the vehicle's position and driving conditions using the camera 42 and sensors of the smart device 14, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The prediction unit performs demand forecasting and delivery route optimization using the specific processing unit 290 of the data processing device 12. The driving unit controls the automatic driving of the truck using the control unit 46A of the smart device 14. The data collection unit collects data obtained from the sensors of the smart device 14 and stores it in the database 24 of the data processing device 12. The safety unit implements safety measures using the camera 42 and LIDAR of the smart device 14, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The provision unit provides information using the display 40A and speaker 40B of the smart device 14. The data collection unit estimates the user's emotions using the camera 42 and microphone 38B of the smart device 14, and adjusts the frequency of data collection using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the monitoring unit, prediction unit, driving unit, data collection unit, safety unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the monitoring unit monitors the vehicle's position and driving conditions using the camera 42 and sensors of the smart glasses 214, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The prediction unit performs demand forecasting and delivery route optimization using the specific processing unit 290 of the data processing device 12. The driving unit controls the automatic driving of the truck using the control unit 46A of the smart glasses 214. The data collection unit collects data obtained from the sensors of the smart glasses 214 and stores it in the database 24 of the data processing device 12. The safety unit implements safety measures using the camera 42 and LIDAR of the smart glasses 214, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The provision unit provides information using the display and speaker of the smart glasses 214. The data collection unit estimates the user's emotions using the camera 42 and microphone 238 of the smart glasses 214, and adjusts the frequency of data collection using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the monitoring unit, prediction unit, driving unit, data collection unit, safety unit, and provision unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the monitoring unit monitors the vehicle's position and driving conditions using the camera 42 and sensors of the headset terminal 314, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The prediction unit performs demand forecasting and delivery route optimization using the specific processing unit 290 of the data processing device 12. The driving unit controls the automatic driving of the truck using the control unit 46A of the headset terminal 314. The data collection unit collects data obtained from the sensors of the headset terminal 314 and stores it in the database 24 of the data processing device 12. The safety unit implements safety measures using the camera 42 and LIDAR of the headset terminal 314, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The provision unit provides information using the display 343 and speaker 240 of the headset terminal 314. The data collection unit estimates the user's emotions using the camera 42 and microphone 238 of the headset terminal 314, and adjusts the frequency of data collection using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the monitoring unit, prediction unit, driving unit, data collection unit, safety unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the monitoring unit monitors the vehicle's position and driving conditions using the camera 42 and sensors of the robot 414, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The prediction unit performs demand forecasting and delivery route optimization using the specific processing unit 290 of the data processing device 12. The driving unit controls the automatic driving of the truck using the control unit 46A of the robot 414. The data collection unit collects data obtained from the robot 414's sensors and stores it in the database 24 of the data processing device 12. The safety unit implements safety measures using the camera 42 and LIDAR of the robot 414, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The provision unit provides information using the display and speaker 240 of the robot 414. The data collection unit estimates the user's emotions using the camera 42 and microphone 238 of the robot 414, and adjusts the frequency of data collection using the specific processing unit 290 of the data processing device 12.

[0134] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0135] The monitoring unit can improve monitoring accuracy by taking geographical obstacles into account when acquiring vehicle position information. For example, the monitoring unit can apply a correction algorithm to improve the accuracy of position information in mountainous areas or areas affected by high-rise buildings. Furthermore, the monitoring unit can supplement the position information using other sensors (e.g., inertial measurement units or dead reckoning) in places where GPS signals are difficult to reach, such as inside tunnels or underground parking lots. Furthermore, the monitoring unit can monitor the effects of geographical obstacles in real time and make corrections as necessary. This allows the monitoring unit to improve monitoring accuracy by taking geographical obstacles into account.

[0136] When making a demand forecast, the forecasting unit can improve the accuracy of the forecast by taking into account a local event calendar. For example, the forecasting unit can collect calendar information about local festivals, sporting events, etc., and reflect this information in the demand forecast. The forecasting unit can also adjust the demand forecast by taking into account the scale of the event and the number of participants. Furthermore, the forecasting unit can propose an optimal delivery route depending on the location and time of the event. This allows the forecasting unit to improve the accuracy of the demand forecast by taking into account a local event calendar.

[0137] The driving unit can provide guidance on eco-driving when controlling the driving of the vehicle. For example, the driving unit can suggest driving methods in real time to maximize fuel efficiency. The driving unit can also adjust the driving style to avoid sudden acceleration and braking. Furthermore, when providing guidance on eco-driving, the driving unit can provide feedback to the driver and support the improvement of driving skills. This allows the driving unit to provide guidance on eco-driving and improve fuel efficiency.

[0138] When monitoring the vehicle's driving status, the monitoring unit can perform analysis taking into account road congestion. For example, the monitoring unit can evaluate the road congestion status based on data obtained from real-time traffic sensors. The monitoring unit can also suggest driving routes that avoid congested roads. Furthermore, the monitoring unit can adjust the driving speed according to the congestion status to support safe driving. This allows the monitoring unit to analyze the driving status taking into account road congestion.

[0139] When making a demand forecast, the prediction unit can improve the accuracy of the forecast by taking into account the user's purchase history. For example, the prediction unit can analyze the user's past purchase history and reflect it in the demand forecast. The prediction unit can also adjust the demand forecast by taking into account the user's purchasing patterns. Furthermore, the prediction unit can analyze the purchasing trends of a specific user group and improve the accuracy of the demand forecast. In this way, the prediction unit can improve the accuracy of the demand forecast by taking into account the user's purchase history.

[0140] The monitoring unit can estimate the user's emotions and adjust the notification method of the monitoring results based on the estimated user's emotions. For example, the monitoring unit can reduce notifications when the user is feeling stressed. Alternatively, the monitoring unit can provide detailed notifications when the user is relaxed. Furthermore, the monitoring unit can adjust the timing of notifications according to the user's emotions to reduce the burden on the user. This allows the monitoring unit to adjust the notification method of the monitoring results based on the user's emotions.

[0141] The prediction unit can estimate the user's emotions and adjust the way in which the demand forecast results are displayed based on the estimated user's emotions. For example, if the user is feeling anxious, the prediction unit can provide a concise and easy-to-understand display. If the user is interested, the prediction unit can provide detailed information. Furthermore, the prediction unit can adjust the display color and font size according to the user's emotions to improve visibility. This allows the prediction unit to adjust the way in which the demand forecast results are displayed based on the user's emotions.

[0142] The driving unit can estimate the user's emotions and adjust the driving assist function based on the estimated user's emotions. For example, the driving unit can strengthen the driving assist when the user is nervous, or reduce the driving assist when the user is relaxed. Furthermore, the driving unit can adjust the feedback method of the driving assist according to the user's emotions to improve the user's driving experience. This allows the driving unit to adjust the driving assist function based on the user's emotions.

[0143] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user's emotions. For example, the data collection unit can moderate data collection when the user is concentrating, or actively collect data when the user is relaxed. Furthermore, the data collection unit can adjust the frequency of data collection according to the user's emotions to reduce the burden on the user. This allows the data collection unit to adjust the timing of data collection based on the user's emotions.

[0144] The providing unit can estimate the user's emotion and adjust the format of the information to be provided based on the estimated user's emotion. For example, if the user is tired, the providing unit can provide concise, highly visible information. Also, if the user is interested, the providing unit can provide detailed information. Furthermore, the providing unit can adjust the way information is displayed according to the user's emotion to help the user understand. This allows the providing unit to adjust the format of the information to be provided based on the user's emotion.

[0145] The processing flow of the second embodiment will be briefly explained below.

[0146] Step 1: The monitoring unit monitors the vehicle's location, driving conditions, traffic congestion information, and fuel consumption. For example, the monitoring unit analyzes information obtained from cameras and sensors installed in the vehicle to grasp the vehicle's status in real time. The monitoring unit can also obtain vehicle location information from GPS data and analyze driving conditions from camera footage. Furthermore, the monitoring unit can obtain fuel consumption data from sensors to support efficient driving. Step 2: The prediction unit performs demand prediction and delivery route optimization processing based on the information monitored by the monitoring unit. For example, the prediction unit performs demand prediction based on past delivery data and current traffic conditions, and calculates the optimal delivery route. The prediction unit can also perform demand prediction using statistical models and machine learning algorithms. Step 3: The driving unit automatically drives the truck based on the results obtained by the prediction unit. For example, the driving unit selects the optimal speed and route based on the vehicle's location information and traffic conditions, and drives safely. The driving unit can use autonomous driving technology to control the truck's driving and drive it to its destination.

[0147] 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.

[0148] 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> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0151] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0152] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0153] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0154] 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.

[0155] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0156] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0161] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0163] 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.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0165] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0166] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0167] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0168] 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.

[0169] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0170] 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.

[0171] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0172] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0173] 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.

[0174] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0175] 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.

[0176] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0177] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0178] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0179] 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.

[0180] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0181] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0182] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0183] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0184] 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.

[0185] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0186] 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.

[0187] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0188] 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 image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0189] 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.

[0190] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0191] 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.

[0192] 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.

[0193] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0194] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0195] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0196] 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.

[0197] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0198] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0199] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0200] 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.

[0201] FIG. 9 illustrates 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 behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions 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.

[0202] 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.

[0203] 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).

[0204] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, 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 expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0205] 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."

[0206] 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.

[0207] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, 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. A processor also includes 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.

[0212] The hardware resource that executes the specific process 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 process may be a single processor.

[0213] 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.

[0214] 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.

[0215] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0216] 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, in order to avoid confusion and to 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.

[0217] 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.

[0218] [Explanation of symbols]

[0219] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a monitoring unit that monitors the vehicle's location, driving status, traffic congestion information, and fuel consumption; a prediction unit that performs demand prediction and delivery route optimization processing based on the information monitored by the monitoring unit; a driving unit that automatically drives a truck based on the results obtained by the prediction unit; Equipped with A system characterized by:

2. The monitoring unit Analyzes information obtained from cameras and sensors mounted on the vehicle to instantly grasp the vehicle's status 2. The system of claim 1.

3. The prediction unit Demand forecasts are made based on past delivery data and current traffic conditions, and optimized delivery routes are calculated.

2. The system of claim 1.

4. The driving unit Drive safely by selecting the optimal speed and route based on the vehicle's location and traffic conditions 2. The system of claim 1.

5. The monitoring unit Fuel consumption data is obtained from sensors to support efficient driving 2. The system of claim 1.

6. Equipped with a data collection unit that collects data used for demand forecasting and delivery route optimization 2. The system of claim 1.

7. Establish a safety measures section that describes the specific details of autonomous driving technology and safety measures.

2. The system of claim 1.

8. Establish a department that provides information to clarify the background and issues of the Logistics 2024 problem 2. The system of claim 1.

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