System

Abstract

An object of a system according to an embodiment is to make an efficient delivery route and plan and minimize an environmental load.SOLUTION: A system according to an embodiment includes a traffic information analysis unit, a route generation unit, a stock prediction unit, a delivery planning unit, and an eco-transportation proposal unit. The traffic information analysis unit analyzes real-time traffic information. The route generation unit generates an optimal delivery route based on the traffic information analyzed by the traffic information analysis unit. The inventory forecasting component analyzes inventory levels and demand forecasts. The delivery planning unit makes an optimal delivery plan based on the inventory level and the demand forecast analyzed by the inventory forecasting unit. The eco-friendly transportation proposal unit proposes the most eco-friendly transportation method in consideration of the cost and efficiency of the transportation method and the environmental load.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, it has been difficult for the delivery industry to create efficient delivery routes and plans while minimizing environmental impact.

[0005] The system according to the embodiment aims to create efficient delivery routes and plans, and minimize the environmental impact. [Means for solving the problem]

[0006] The system according to the embodiment includes a traffic information analysis unit, a route generation unit, an inventory forecasting unit, a delivery planning unit, and an eco-transportation suggestion unit. The traffic information analysis unit analyzes real-time traffic information. The route generation unit generates an optimal delivery route based on the traffic information analyzed by the traffic information analysis unit. The inventory forecasting unit analyzes inventory levels and demand forecasts. The delivery planning unit creates an optimal delivery plan based on the inventory levels and demand forecasts analyzed by the inventory forecasting unit. The eco-transportation suggestion unit suggests the most eco-friendly transportation method, taking into account the cost, efficiency, and environmental impact of the transportation method. [Effects of the Invention]

[0007] The system according to the embodiment can create efficient delivery routes and plans, minimizing the burden on the environment. [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 delivery management system according to an embodiment of the present invention utilizes generative AI and IoT technology to provide a smart, environmentally friendly management system for the delivery industry. This system analyzes real-time traffic information, inventory levels, demand forecasts, and other data to automatically generate optimal delivery routes and plans. This enables the delivery management system to achieve efficient and speedy delivery while reducing environmental impact.

[0029] A delivery management system according to an embodiment includes a traffic information analysis unit, a route generation unit, an inventory forecasting unit, a delivery planning unit, and an eco-friendly transportation proposal unit. The traffic information analysis unit analyzes real-time traffic information. For example, it analyzes traffic congestion information and accident information to select the most efficient route. The traffic information analysis unit collects traffic information, and a generation AI generates an optimal route based on the traffic information. The route generation unit generates an optimal delivery route based on the traffic information analyzed by the traffic information analysis unit. For example, the generation AI proposes the shortest route based on the traffic information. The route generation unit uses an algorithm for generating an optimal delivery route based on the traffic information. The inventory forecasting unit analyzes inventory levels and demand forecasts. For example, the generation AI analyzes inventory data and demand forecast data to create an optimal delivery plan. The inventory forecasting unit uses an algorithm for creating an optimal delivery plan based on the inventory levels and demand forecasts. The delivery planning unit creates an optimal delivery plan based on the inventory levels and demand forecasts analyzed by the inventory forecasting unit. For example, the generation AI generates an optimal delivery plan based on the inventory data and demand forecast data. The delivery planning unit also uses an algorithm to create an optimal delivery plan based on inventory levels and demand forecasts. The eco-transportation suggestion unit proposes the most eco-friendly delivery method, taking into account the cost, efficiency, and environmental impact of the delivery method. For example, the generation AI analyzes the options for delivery methods and environmental impact data to propose the most eco-friendly delivery method. The eco-transportation suggestion unit also uses an algorithm to propose the optimal delivery method based on the cost, efficiency, and environmental impact of the delivery method. As a result, the delivery management system according to the embodiment can achieve efficient and speedy delivery and reduce environmental impact.

[0030] The traffic information analysis unit can analyze not only traffic information but also weather data and event information to generate the optimal route. For example, the generation AI in the traffic information analysis unit analyzes weather data in addition to traffic information to generate a route that avoids bad weather such as rain and snow. For example, it obtains weather forecast data in real time and combines it with traffic conditions to propose the optimal route. The generation AI in the traffic information analysis unit also analyzes event information to generate a route that avoids areas where large events are held. For example, it obtains information on concerts and sporting events and proposes a route that avoids crowds. The generation AI in the traffic information analysis unit also analyzes traffic information, weather data, and event information in an integrated manner to generate the most efficient and safe route. For example, it proposes a composite route that avoids traffic congestion, bad weather, and large-scale events. This makes it possible to generate the optimal route taking weather and event information into account.

[0031] The traffic information analysis unit can learn past traffic patterns and generate routes by predicting future traffic conditions. For example, the generation AI in the traffic information analysis unit learns past traffic data and predicts traffic patterns for specific times of day and days of the week. For example, it can propose routes that avoid rush hour on weekdays in the morning and evening. The generation AI in the traffic information analysis unit also analyzes past traffic accident data and generates routes that avoid areas where accidents are likely to occur. For example, it can propose safe routes based on past accident locations. The generation AI in the traffic information analysis unit also combines past traffic data with real-time traffic information to predict future traffic conditions and generate optimal routes. For example, it can propose routes that avoid areas where congestion is expected based on past data. This makes it possible to predict future traffic conditions based on past data and generate optimal routes.

[0032] The inventory forecasting unit can make demand forecasts taking into account past sales data and seasonality. In the inventory forecasting unit, for example, the generation AI analyzes past sales data and makes demand forecasts taking into account seasonality. For example, it predicts products whose demand will increase at specific times such as Christmas and New Year. In addition, the inventory forecasting unit makes demand forecasts by combining past sales data and weather data. For example, it predicts demand for products that are affected by weather. In addition, the inventory forecasting unit makes demand forecasts by analyzing past sales data and promotion data and taking into account the impact of promotions. For example, it predicts demand during a specific campaign period. This makes it possible to make demand forecasts taking into account past sales data and seasonality.

[0033] The inventory forecasting unit can analyze social media trends and reflect them in demand forecasts. In the inventory forecasting unit, for example, the generation AI analyzes social media posting data, identifies trends, and reflects them in demand forecasts. For example, it predicts demand based on an increase in the number of posts about a specific product or brand. In addition, the generation AI analyzes posts by social media influencers and makes demand forecasts taking their influence into account. For example, it predicts demand for products introduced by influencers. In addition, the generation AI analyzes social media hashtag data, identifies trends, and reflects them in demand forecasts. For example, it predicts demand for products or services for which a specific hashtag is rapidly increasing. This makes it possible to make demand forecasts that take social media trends into account.

[0034] The eco-transportation proposal unit can perform a life cycle assessment of transportation methods and propose the most environmentally friendly method. For example, the generation AI can perform a life cycle assessment of transportation methods and propose methods to minimize carbon dioxide emissions. For example, it can prioritize routes that use electric vehicles or bicycles. The eco-transportation proposal unit can also perform a life cycle assessment of transportation methods and propose methods to minimize energy consumption. For example, it can recommend fuel-efficient vehicles and eco-driving. The eco-transportation proposal unit can also perform a life cycle assessment of transportation methods and propose methods to minimize waste generation. For example, it can recommend transportation methods that use recyclable packaging materials. This allows the generation AI to perform a life cycle assessment of transportation methods and propose the most environmentally friendly method.

[0035] The eco-transportation proposal unit can analyze the usage status of renewable energy and propose eco-friendly transportation methods. For example, the generation AI in the eco-transportation proposal unit analyzes the usage status of renewable energy and proposes transportation methods that use solar power or wind power. For example, it generates a route that passes through a charging station that uses renewable energy. The generation AI in the eco-transportation proposal unit also analyzes the supply status of renewable energy in real time and proposes energy-efficient transportation methods. For example, it generates a route that transports during times when there is a high supply of renewable energy. The generation AI in the eco-transportation proposal unit also analyzes the usage status of renewable energy and proposes eco-friendly transportation methods. For example, it prioritizes routes that use electric vehicles or hybrid vehicles. In this way, the usage status of renewable energy can be analyzed and eco-friendly transportation methods can be proposed.

[0036] The eco-friendly transportation proposal unit can propose a combination of different transportation means. For example, in the eco-friendly transportation proposal unit, the generation AI proposes a transportation method that combines drones and self-driving cars. For example, it combines short-distance delivery by drone with long-distance delivery by self-driving car. In addition, in the eco-friendly transportation proposal unit, the generation AI proposes a transportation method that combines electric vehicles and bicycles. For example, it combines main routes by electric vehicles with last-mile delivery by bicycle. In addition, in the eco-friendly transportation proposal unit, the generation AI proposes a transportation method that combines public transportation and self-driving cars. For example, it combines main routes by public transportation with last-mile delivery by self-driving car. This makes it possible to propose combinations of different transportation means.

[0037] The eco-transportation proposal unit can analyze the balance between cost and environmental impact of transportation methods and propose the optimal method. For example, the generation AI in the eco-transportation proposal unit analyzes the cost and environmental impact of transportation methods and proposes a method that strikes the optimal balance. For example, it selects a transportation method that is low in cost and has a low environmental impact. The generation AI in the eco-transportation proposal unit also analyzes the cost and environmental impact of transportation methods in real time and proposes the optimal method. For example, it proposes a transportation method that takes fuel costs and carbon dioxide emissions into consideration. The generation AI in the eco-transportation proposal unit also analyzes the cost and environmental impact of transportation methods and proposes the optimal method from a long-term perspective. For example, it selects a transportation method that has a high initial cost but a low environmental impact in the long term. This makes it possible to propose the optimal transportation method that takes into consideration the balance between cost and environmental impact.

[0038] The traffic information analysis unit also analyzes public transportation data and can incorporate public transportation into optimal routes. For example, the generation AI in the traffic information analysis unit analyzes bus and train operation information and incorporates public transportation into optimal routes. For example, it proposes a route that uses public transportation and avoids areas where congestion is predicted. The generation AI in the traffic information analysis unit also analyzes public transportation schedules and generates a route that allows for smooth transfers. For example, it proposes a route with good connections between buses and trains. The generation AI in the traffic information analysis unit also analyzes the congestion status of public transportation and generates a route that avoids congestion. For example, it proposes a route that uses less crowded times and routes. This makes it possible to generate optimal routes that incorporate public transportation.

[0039] The traffic information analysis unit allows multiple delivery vehicles to cooperate and generate optimal routes. In the traffic information analysis unit, for example, the generation AI analyzes the location information of multiple delivery vehicles and generates efficient routes. For example, when multiple vehicles cover the same area, it proposes a route that avoids overlap. In addition, the traffic information analysis unit allows the generation AI to analyze the package information of multiple delivery vehicles and generate optimal routes. For example, when the package recipients are close to each other, it proposes a route that allows for efficient delivery. In addition, the traffic information analysis unit allows the generation AI to analyze the operation schedules of multiple delivery vehicles and generate efficient routes through cooperation. For example, it proposes a route that takes into account the handover of packages between vehicles. This allows multiple delivery vehicles to cooperate and generate optimal routes.

[0040] The inventory forecasting unit can integrate inventory data from different regions and perform global demand forecasts. For example, the generation AI in the inventory forecasting unit integrates inventory data from different regions and performs global demand forecasts. For example, it grasps the inventory status of each region in real time and predicts fluctuations in demand. The generation AI in the inventory forecasting unit also analyzes sales data from different regions and grasps demand patterns for each region to perform global demand forecasts. For example, it predicts demand in other regions based on increased demand in a specific region. The generation AI in the inventory forecasting unit also analyzes economic data from different regions and performs global demand forecasts that take economic conditions into account. For example, it predicts increased demand in regions where economic growth is expected. This allows inventory data from different regions to be integrated and global demand forecasts to be performed.

[0041] The inventory forecasting unit can analyze data from the entire supply chain and reflect it in the demand forecast. In the inventory forecasting unit, for example, the generation AI analyzes data from the entire supply chain and reflects it in the demand forecast. For example, a demand forecast is made taking into account the supply status of raw materials and manufacturing capacity. In addition, the inventory forecasting unit has the generation AI integrate data from each stage of the supply chain and reflect it in the demand forecast. For example, it predicts demand by analyzing the entire process from manufacturing to delivery. In addition, the inventory forecasting unit has the generation AI analyze risk data from the supply chain and make a demand forecast that takes risk into account. For example, it predicts demand taking into account natural disasters and political risks. This makes it possible to analyze data from the entire supply chain and reflect it in the demand forecast.

[0042] The eco-transportation proposal unit can analyze environmental impact data from different industries and propose best practices. For example, the generation AI in the eco-transportation proposal unit analyzes environmental impact data from different industries and proposes the most environmentally friendly transportation method. For example, it selects a transportation method based on environmental impact data from manufacturing or agriculture. The generation AI in the eco-transportation proposal unit also analyzes environmental impact data from different industries and proposes common best practices. For example, it proposes eco-friendly transportation methods that are effective across multiple industries. The generation AI in the eco-transportation proposal unit also analyzes environmental impact data from different industries and proposes transportation methods that are tailored to the characteristics of each industry. For example, it applies transportation methods that are effective in a particular industry to other industries. This makes it possible to analyze environmental impact data from different industries and propose best practices.

[0043] The Eco-Transportation Proposal Department can analyze the environmental impact of the entire supply chain and propose sustainable transportation methods. For example, the generation AI in the Eco-Transportation Proposal Department analyzes environmental impact data for the entire supply chain and proposes sustainable transportation methods. For example, it selects a transportation method that takes into account the entire process from the supply of raw materials to the delivery of products. The generation AI in the Eco-Transportation Proposal Department also analyzes the environmental impact of each stage of the supply chain and proposes the most environmentally friendly transportation method. For example, it optimizes the entire process from manufacturing to delivery. The generation AI in the Eco-Transportation Proposal Department also analyzes risk data for the supply chain and proposes sustainable transportation methods that take risks into account. For example, it selects a transportation method that takes natural disasters and political risks into account. This makes it possible to analyze the environmental impact of the entire supply chain and propose sustainable transportation methods.

[0044] The traffic information analysis unit uses IoT sensors to collect vehicle fuel efficiency data in real time, and the generation AI can then use that data to suggest optimal driving methods. For example, the traffic information analysis unit uses IoT sensors to collect vehicle fuel efficiency data in real time, and the generation AI can then use that data to suggest eco-driving methods. For example, it can suggest driving methods that avoid sudden acceleration and braking. The traffic information analysis unit also uses IoT sensors to analyze vehicle fuel efficiency data and suggest fuel-efficient routes. For example, it can select routes that are flat and have less traffic congestion. The traffic information analysis unit also uses IoT sensors to collect vehicle fuel efficiency data, and the generation AI can then use that data to suggest vehicle maintenance. For example, it can suggest tire pressure and when to change engine oil. This makes it possible to suggest optimal driving methods based on vehicle fuel efficiency data.

[0045] The traffic information analysis unit uses IoT sensors to monitor road conditions in real time, and the generation AI can use this information to generate the optimal route. For example, the traffic information analysis unit uses IoT sensors to monitor road conditions in real time, and the generation AI uses this information to generate the optimal route. For example, it proposes a route that takes into account road unevenness and construction information. The traffic information analysis unit also uses IoT sensors to analyze road conditions and propose routes with a low risk of traffic accidents. For example, it selects a route that avoids slippery roads and areas with poor visibility. The traffic information analysis unit also uses IoT sensors to collect road condition information in real time, and the generation AI uses this information to propose road maintenance. For example, it identifies damaged areas on the road and proposes areas that require repair. This makes it possible to monitor road conditions in real time and generate the optimal route.

[0046] The inventory prediction unit uses IoT sensors to monitor the temperature and humidity in the warehouse, and the generation AI can use this information to perform optimal inventory management. For example, the inventory prediction unit uses IoT sensors to monitor the temperature and humidity in the warehouse in real time, and the generation AI uses this information to perform optimal inventory management. For example, it adjusts the temperature and humidity to keep them within an appropriate range. The inventory prediction unit also uses IoT sensors to analyze environmental data in the warehouse and makes suggestions to reduce the risk of inventory deterioration. For example, it activates the cooling system when the temperature or humidity is high. The inventory prediction unit also uses IoT sensors to monitor the temperature and humidity in the warehouse, and the generation AI uses this information to optimize inventory placement. For example, it places products that are sensitive to temperature and humidity in appropriate locations. This allows the temperature and humidity in the warehouse to be monitored and optimal inventory management to be performed.

[0047] The inventory prediction unit uses IoT sensors to collect vehicle maintenance data, and the generation AI can then suggest preventive maintenance based on that data. For example, the inventory prediction unit uses IoT sensors to collect vehicle maintenance data in real time, and the generation AI can then suggest preventive maintenance based on that data. For example, it can detect engine abnormalities and suggest early repairs. The inventory prediction unit also uses IoT sensors to analyze vehicle operation data and make suggestions to optimize the timing of maintenance. For example, it can set maintenance times based on mileage and usage conditions. The inventory prediction unit also uses IoT sensors to collect vehicle maintenance data, and the generation AI can then use that data to create a maintenance plan. For example, it can suggest a schedule for efficiently performing maintenance on multiple vehicles. This makes it possible to suggest preventive maintenance based on vehicle maintenance data.

[0048] The eco-transportation suggestion unit can monitor carbon dioxide emissions in real time and suggest the optimal transportation method. For example, the generation AI in the eco-transportation suggestion unit monitors the carbon dioxide emissions of vehicles in real time and suggests transportation methods with low emissions. For example, it prioritizes routes that use electric or hybrid vehicles. The generation AI in the eco-transportation suggestion unit also analyzes the carbon dioxide emissions of transportation routes and suggests the most environmentally friendly route. For example, it selects routes that avoid traffic congestion or short routes. The generation AI in the eco-transportation suggestion unit also monitors carbon dioxide emissions in real time and adjusts the transportation method if emissions are high. For example, it sends a notification recommending eco-driving. This makes it possible to monitor carbon dioxide emissions in real time and suggest the optimal transportation method.

[0049] The eco-transportation suggestion unit can analyze energy consumption data and suggest energy-efficient transportation methods. For example, the generation AI in the eco-transportation suggestion unit analyzes vehicle energy consumption data and suggests energy-efficient transportation methods. For example, it recommends fuel-efficient vehicles and eco-driving. The generation AI in the eco-transportation suggestion unit also analyzes energy consumption data for transportation routes and suggests the most energy-efficient route. For example, it selects a route on flat roads or with less traffic congestion. The generation AI in the eco-transportation suggestion unit also monitors energy consumption data in real time and adjusts the transportation method if energy consumption is high. For example, it sends a notification recommending an energy-efficient driving method. This makes it possible to analyze energy consumption data and suggest energy-efficient transportation methods.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The delivery management system can further include a health management unit that monitors the driver's health condition. For example, the health management unit monitors the driver's heart rate and body temperature in real time and issues an alert if an abnormality is detected. The health management unit can also suggest rest and exercise based on the driver's health condition. For example, it can suggest appropriate rest times when driving for long periods of time. The health management unit can also analyze the driver's health data and provide advice to improve their health. For example, it can provide advice on diet and exercise. This helps maintain the driver's health and ensure efficient delivery.

[0052] The delivery management system can further include a customer analysis unit that analyzes customer purchase history. The customer analysis unit, for example, analyzes a customer's past purchase history and predicts their next purchase. The customer analysis unit can also suggest individual promotions based on the customer's purchasing patterns. For example, for a customer who frequently purchases a particular product, it can suggest a promotion related to that product. The customer analysis unit can also optimize inventory management based on the customer's purchase history. For example, it can adjust inventory so that a particular product does not run out. This makes it possible to utilize customer purchase history to achieve efficient inventory management and promotions.

[0053] The delivery management system can further include a maintenance management unit that manages the maintenance of delivery vehicles. The maintenance management unit, for example, monitors the vehicle's mileage and usage status in real time to optimize the timing of maintenance. The maintenance management unit can also detect vehicle abnormalities early and suggest preventive maintenance. For example, if an engine abnormality is detected, it can suggest early repair. The maintenance management unit can also manage the vehicle's maintenance history and create an efficient maintenance plan. For example, it can suggest a schedule for efficiently performing maintenance on multiple vehicles. This allows for optimizing vehicle maintenance and achieving efficient delivery.

[0054] The delivery management system can further include a fuel efficiency management unit that optimizes the fuel efficiency of delivery vehicles. The fuel efficiency management unit, for example, monitors the vehicle's fuel efficiency data in real time and suggests fuel-efficient driving methods. The fuel efficiency management unit can also analyze the vehicle's fuel efficiency data and suggest fuel-efficient routes. For example, it can select routes on flat roads or with less traffic congestion. The fuel efficiency management unit can also make vehicle maintenance suggestions based on the vehicle's fuel efficiency data. For example, it can suggest tire pressure and engine oil change times. This optimizes the vehicle's fuel efficiency and enables efficient delivery.

[0055] The delivery management system can further include a schedule management unit that optimizes the operation schedules of delivery vehicles. The schedule management unit, for example, monitors the operation schedules of delivery vehicles in real time and proposes efficient schedules. The schedule management unit can also analyze the operation data of delivery vehicles and generate optimal operation schedules. For example, when multiple vehicles cover the same area, it proposes a schedule that avoids overlap. The schedule management unit can also create efficient delivery plans based on the operation schedules of delivery vehicles. For example, when the recipient of the package is close, it proposes a schedule that allows for efficient delivery. This allows the operation schedules of delivery vehicles to be optimized and efficient delivery to be achieved.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The traffic information analysis unit analyzes real-time traffic information. For example, it analyzes traffic congestion and accident information and selects the most efficient route. The traffic information analysis unit also collects traffic information, and the generation AI uses it to generate the optimal route. Step 2: The route generation unit generates the optimal delivery route based on the traffic information analyzed by the traffic information analysis unit. For example, the generation AI proposes the shortest route based on the traffic information. The route generation unit also uses an algorithm to generate the optimal delivery route based on the traffic information. Step 3: The inventory forecasting unit analyzes inventory levels and demand forecasts. For example, a generative AI analyzes inventory data and demand forecast data to create an optimal delivery plan. The inventory forecasting unit also uses an algorithm to create an optimal delivery plan based on inventory levels and demand forecasts. Step 4: The delivery planning unit creates an optimal delivery plan based on the inventory levels and demand forecasts analyzed by the inventory forecasting unit. For example, a generation AI generates an optimal delivery plan based on inventory data and demand forecast data. The delivery planning unit also uses an algorithm to create an optimal delivery plan based on inventory levels and demand forecasts. Step 5: The Eco-Friendly Transportation Proposal Unit proposes the most eco-friendly transportation method, taking into account the cost, efficiency, and environmental impact of each method. For example, the Generative AI analyzes the transportation options and environmental impact data to propose the most eco-friendly transportation method. The Eco-Friendly Transportation Proposal Unit also uses an algorithm to propose the optimal transportation method based on the cost, efficiency, and environmental impact of each method.

[0058] (Example 2) A delivery management system according to an embodiment of the present invention utilizes generative AI and IoT technology to provide a smart, environmentally friendly management system for the delivery industry. This system analyzes real-time traffic information, inventory levels, demand forecasts, and other data to automatically generate optimal delivery routes and plans. This enables the delivery management system to achieve efficient and speedy delivery while reducing environmental impact.

[0059] A delivery management system according to an embodiment includes a traffic information analysis unit, a route generation unit, an inventory forecasting unit, a delivery planning unit, and an eco-friendly transportation proposal unit. The traffic information analysis unit analyzes real-time traffic information. For example, it analyzes traffic congestion information and accident information to select the most efficient route. The traffic information analysis unit collects traffic information, and a generation AI generates an optimal route based on the traffic information. The route generation unit generates an optimal delivery route based on the traffic information analyzed by the traffic information analysis unit. For example, the generation AI proposes the shortest route based on the traffic information. The route generation unit uses an algorithm for generating an optimal delivery route based on the traffic information. The inventory forecasting unit analyzes inventory levels and demand forecasts. For example, the generation AI analyzes inventory data and demand forecast data to create an optimal delivery plan. The inventory forecasting unit uses an algorithm for creating an optimal delivery plan based on the inventory levels and demand forecasts. The delivery planning unit creates an optimal delivery plan based on the inventory levels and demand forecasts analyzed by the inventory forecasting unit. For example, the generation AI generates an optimal delivery plan based on the inventory data and demand forecast data. The delivery planning unit also uses an algorithm to create an optimal delivery plan based on inventory levels and demand forecasts. The eco-transportation suggestion unit proposes the most eco-friendly delivery method, taking into account the cost, efficiency, and environmental impact of the delivery method. For example, the generation AI analyzes the options for delivery methods and environmental impact data to propose the most eco-friendly delivery method. The eco-transportation suggestion unit also uses an algorithm to propose the optimal delivery method based on the cost, efficiency, and environmental impact of the delivery method. As a result, the delivery management system according to the embodiment can achieve efficient and speedy delivery and reduce environmental impact.

[0060] The traffic information analysis unit can analyze not only traffic information but also weather data and event information to generate the optimal route. For example, the generation AI in the traffic information analysis unit analyzes weather data in addition to traffic information to generate a route that avoids bad weather such as rain and snow. For example, it obtains weather forecast data in real time and combines it with traffic conditions to propose the optimal route. The generation AI in the traffic information analysis unit also analyzes event information to generate a route that avoids areas where large events are held. For example, it obtains information on concerts and sporting events and proposes a route that avoids crowds. The generation AI in the traffic information analysis unit also analyzes traffic information, weather data, and event information in an integrated manner to generate the most efficient and safe route. For example, it proposes a composite route that avoids traffic congestion, bad weather, and large-scale events. This makes it possible to generate the optimal route taking weather and event information into account.

[0061] The traffic information analysis unit can learn past traffic patterns and generate routes by predicting future traffic conditions. For example, the generation AI in the traffic information analysis unit learns past traffic data and predicts traffic patterns for specific times of day and days of the week. For example, it can propose routes that avoid rush hour on weekdays in the morning and evening. The generation AI in the traffic information analysis unit also analyzes past traffic accident data and generates routes that avoid areas where accidents are likely to occur. For example, it can propose safe routes based on past accident locations. The generation AI in the traffic information analysis unit also combines past traffic data with real-time traffic information to predict future traffic conditions and generate optimal routes. For example, it can propose routes that avoid areas where congestion is expected based on past data. This makes it possible to predict future traffic conditions based on past data and generate optimal routes.

[0062] The traffic information analysis unit can use the emotion estimation function to estimate the driver's stress level and suggest a route that reduces stress. For example, the traffic information analysis unit can use the emotion estimation function to monitor the driver's stress level in real time and suggest a relaxing route if the driver is highly stressed. For example, it can select a scenic route or a less congested route. The traffic information analysis unit can also suggest a route that includes rest stops based on the driver's stress level. For example, if the driver is tired, it can generate a route that includes appropriate rest stops. The traffic information analysis unit can also use the emotion estimation function to suggest music or relaxation content to reduce the driver's stress level. For example, it can play relaxing music if the driver is highly stressed. This makes it possible to suggest a route that reduces the driver's stress.

[0063] The inventory forecasting unit can make demand forecasts taking into account past sales data and seasonality. In the inventory forecasting unit, for example, the generation AI analyzes past sales data and makes demand forecasts taking into account seasonality. For example, it predicts products whose demand will increase at specific times such as Christmas and New Year. In addition, the inventory forecasting unit makes demand forecasts by combining past sales data and weather data. For example, it predicts demand for products that are affected by weather. In addition, the inventory forecasting unit makes demand forecasts by analyzing past sales data and promotion data and taking into account the impact of promotions. For example, it predicts demand during a specific campaign period. This makes it possible to make demand forecasts taking into account past sales data and seasonality.

[0064] The inventory forecasting unit can analyze social media trends and reflect them in demand forecasts. In the inventory forecasting unit, for example, the generation AI analyzes social media posting data, identifies trends, and reflects them in demand forecasts. For example, it predicts demand based on an increase in the number of posts about a specific product or brand. In addition, the generation AI analyzes posts by social media influencers and makes demand forecasts taking their influence into account. For example, it predicts demand for products introduced by influencers. In addition, the generation AI analyzes social media hashtag data, identifies trends, and reflects them in demand forecasts. For example, it predicts demand for products or services for which a specific hashtag is rapidly increasing. This makes it possible to make demand forecasts that take social media trends into account.

[0065] The inventory forecasting unit can use the emotion estimation function to estimate customer purchasing intent and reflect this in demand forecasting. The inventory forecasting unit, for example, uses the emotion estimation function to monitor customer purchasing intent in real time and reflect this in demand forecasting. For example, it predicts products for which demand will increase when customers are showing positive emotions. The inventory forecasting unit also predicts the effectiveness of promotions and campaigns based on customers' purchasing intent. For example, it predicts demand for products in which customers are interested. The inventory forecasting unit also uses the emotion estimation function to identify factors that reduce customers' purchasing intent and performs demand forecasting that takes these effects into account. For example, it predicts demand for products for which customers are showing negative emotions. This makes it possible to make demand forecasts that take customers' purchasing intent into account.

[0066] The eco-transportation proposal unit can perform a life cycle assessment of transportation methods and propose the most environmentally friendly method. For example, the generation AI can perform a life cycle assessment of transportation methods and propose methods to minimize carbon dioxide emissions. For example, it can prioritize routes that use electric vehicles or bicycles. The eco-transportation proposal unit can also perform a life cycle assessment of transportation methods and propose methods to minimize energy consumption. For example, it can recommend fuel-efficient vehicles and eco-driving. The eco-transportation proposal unit can also perform a life cycle assessment of transportation methods and propose methods to minimize waste generation. For example, it can recommend transportation methods that use recyclable packaging materials. This allows the generation AI to perform a life cycle assessment of transportation methods and propose the most environmentally friendly method.

[0067] The eco-transportation proposal unit can analyze the usage status of renewable energy and propose eco-friendly transportation methods. For example, the generation AI in the eco-transportation proposal unit analyzes the usage status of renewable energy and proposes transportation methods that use solar power or wind power. For example, it generates a route that passes through a charging station that uses renewable energy. The generation AI in the eco-transportation proposal unit also analyzes the supply status of renewable energy in real time and proposes energy-efficient transportation methods. For example, it generates a route that transports during times when there is a high supply of renewable energy. The generation AI in the eco-transportation proposal unit also analyzes the usage status of renewable energy and proposes eco-friendly transportation methods. For example, it prioritizes routes that use electric vehicles or hybrid vehicles. In this way, the usage status of renewable energy can be analyzed and eco-friendly transportation methods can be proposed.

[0068] The eco-transportation suggestion unit can use the emotion estimation function to estimate the customer's environmental awareness and suggest a transportation method based on that. For example, the eco-transportation suggestion unit can use the emotion estimation function to monitor the customer's environmental awareness in real time and suggest an eco-friendly transportation method if the customer is highly environmentally conscious. For example, it can select a route that uses an electric vehicle or bicycle. The eco-transportation suggestion unit also presents options for transportation methods based on the customer's environmental awareness. For example, it can preferentially suggest eco-friendly transportation methods to customers who are highly environmentally conscious. The eco-transportation suggestion unit also uses the emotion estimation function to provide information to improve the customer's environmental awareness. For example, it can send a notification explaining the benefits of eco-friendly transportation methods. In this way, the emotion estimation function can be used to suggest transportation methods that take the customer's environmental awareness into consideration.

[0069] The eco-friendly transportation proposal unit can propose a combination of different transportation means. For example, in the eco-friendly transportation proposal unit, the generation AI proposes a transportation method that combines drones and self-driving cars. For example, it combines short-distance delivery by drone with long-distance delivery by self-driving car. In addition, in the eco-friendly transportation proposal unit, the generation AI proposes a transportation method that combines electric vehicles and bicycles. For example, it combines main routes by electric vehicles with last-mile delivery by bicycle. In addition, in the eco-friendly transportation proposal unit, the generation AI proposes a transportation method that combines public transportation and self-driving cars. For example, it combines main routes by public transportation with last-mile delivery by self-driving car. This makes it possible to propose combinations of different transportation means.

[0070] The eco-transportation proposal unit can analyze the balance between cost and environmental impact of transportation methods and propose the optimal method. For example, the generation AI in the eco-transportation proposal unit analyzes the cost and environmental impact of transportation methods and proposes a method that strikes the optimal balance. For example, it selects a transportation method that is low in cost and has a low environmental impact. The generation AI in the eco-transportation proposal unit also analyzes the cost and environmental impact of transportation methods in real time and proposes the optimal method. For example, it proposes a transportation method that takes fuel costs and carbon dioxide emissions into consideration. The generation AI in the eco-transportation proposal unit also analyzes the cost and environmental impact of transportation methods and proposes the optimal method from a long-term perspective. For example, it selects a transportation method that has a high initial cost but a low environmental impact in the long term. This makes it possible to propose the optimal transportation method that takes into consideration the balance between cost and environmental impact.

[0071] The eco-friendly transportation suggestion unit can use the emotion estimation function to estimate the health status of employees and suggest health-conscious transportation methods. The eco-friendly transportation suggestion unit, for example, uses the emotion estimation function to monitor the health status of employees in real time and suggest health-conscious transportation methods. For example, if fatigue is accumulating, it suggests a route that includes breaks. The eco-friendly transportation suggestion unit also presents transportation method options based on the employee's health status. For example, if the employee's health status is good, it suggests long-distance transportation, and if the employee's health status is poor, it suggests short-distance transportation. The eco-friendly transportation suggestion unit also uses the emotion estimation function to suggest measures to improve the employee's health status and reflects the results in the transportation method. For example, it suggests training or breaks to improve the health status. This makes it possible to suggest transportation methods that take the employee's health status into consideration.

[0072] The traffic information analysis unit also analyzes public transportation data and can incorporate public transportation into optimal routes. For example, the generation AI in the traffic information analysis unit analyzes bus and train operation information and incorporates public transportation into optimal routes. For example, it proposes a route that uses public transportation and avoids areas where congestion is predicted. The generation AI in the traffic information analysis unit also analyzes public transportation schedules and generates a route that allows for smooth transfers. For example, it proposes a route with good connections between buses and trains. The generation AI in the traffic information analysis unit also analyzes the congestion status of public transportation and generates a route that avoids congestion. For example, it proposes a route that uses less crowded times and routes. This makes it possible to generate optimal routes that incorporate public transportation.

[0073] The traffic information analysis unit allows multiple delivery vehicles to cooperate and generate optimal routes. In the traffic information analysis unit, for example, the generation AI analyzes the location information of multiple delivery vehicles and generates efficient routes. For example, when multiple vehicles cover the same area, it proposes a route that avoids overlap. In addition, the traffic information analysis unit allows the generation AI to analyze the package information of multiple delivery vehicles and generate optimal routes. For example, when the package recipients are close to each other, it proposes a route that allows for efficient delivery. In addition, the traffic information analysis unit allows the generation AI to analyze the operation schedules of multiple delivery vehicles and generate efficient routes through cooperation. For example, it proposes a route that takes into account the handover of packages between vehicles. This allows multiple delivery vehicles to cooperate and generate optimal routes.

[0074] The traffic information analysis unit can use the emotion estimation function to estimate customer expectations and propose a route that maximizes customer satisfaction. The traffic information analysis unit, for example, uses the emotion estimation function to monitor customer expectations in real time and propose a speedy delivery route when expectations are high. For example, the traffic information analysis unit selects the shortest route when the customer is in a hurry. The traffic information analysis unit also proposes a route that adjusts delivery time based on customer expectations. For example, it generates a route that allows delivery within a time period specified by the customer. The traffic information analysis unit also uses the emotion estimation function to provide notifications and information to reduce customer expectations. For example, it notifies the customer of delivery status in real time to reduce customer anxiety. This makes it possible to propose a route that takes customer expectations into consideration and maximize customer satisfaction.

[0075] The inventory forecasting unit can integrate inventory data from different regions and perform global demand forecasts. For example, the generation AI in the inventory forecasting unit integrates inventory data from different regions and performs global demand forecasts. For example, it grasps the inventory status of each region in real time and predicts fluctuations in demand. The generation AI in the inventory forecasting unit also analyzes sales data from different regions and grasps demand patterns for each region to perform global demand forecasts. For example, it predicts demand in other regions based on increased demand in a specific region. The generation AI in the inventory forecasting unit also analyzes economic data from different regions and performs global demand forecasts that take economic conditions into account. For example, it predicts increased demand in regions where economic growth is expected. This allows inventory data from different regions to be integrated and global demand forecasts to be performed.

[0076] The inventory forecasting unit can analyze data from the entire supply chain and reflect it in the demand forecast. In the inventory forecasting unit, for example, the generation AI analyzes data from the entire supply chain and reflects it in the demand forecast. For example, a demand forecast is made taking into account the supply status of raw materials and manufacturing capacity. In addition, the inventory forecasting unit has the generation AI integrate data from each stage of the supply chain and reflect it in the demand forecast. For example, it predicts demand by analyzing the entire process from manufacturing to delivery. In addition, the inventory forecasting unit has the generation AI analyze risk data from the supply chain and make a demand forecast that takes risk into account. For example, it predicts demand taking into account natural disasters and political risks. This makes it possible to analyze data from the entire supply chain and reflect it in the demand forecast.

[0077] The inventory prediction unit can use the emotion estimation function to estimate employee motivation and reflect this in inventory management. For example, the inventory prediction unit uses the emotion estimation function to monitor employee motivation in real time and reflect this in inventory management. For example, efficient inventory management is performed when employee motivation is high. The inventory prediction unit also identifies areas for improvement in inventory management based on employee motivation. For example, if motivation is low, the inventory prediction unit reviews the inventory management process. The inventory prediction unit also uses the emotion estimation function to propose measures to improve employee motivation and reflect the results in inventory management. For example, it proposes training or incentives to improve motivation. This makes it possible to manage inventory while taking employee motivation into account.

[0078] The eco-transportation proposal unit can analyze environmental impact data from different industries and propose best practices. For example, the generation AI in the eco-transportation proposal unit analyzes environmental impact data from different industries and proposes the most environmentally friendly transportation method. For example, it selects a transportation method based on environmental impact data from manufacturing or agriculture. The generation AI in the eco-transportation proposal unit also analyzes environmental impact data from different industries and proposes common best practices. For example, it proposes eco-friendly transportation methods that are effective across multiple industries. The generation AI in the eco-transportation proposal unit also analyzes environmental impact data from different industries and proposes transportation methods that are tailored to the characteristics of each industry. For example, it applies transportation methods that are effective in a particular industry to other industries. This makes it possible to analyze environmental impact data from different industries and propose best practices.

[0079] The Eco-Transportation Proposal Department can analyze the environmental impact of the entire supply chain and propose sustainable transportation methods. For example, the generation AI in the Eco-Transportation Proposal Department analyzes environmental impact data for the entire supply chain and proposes sustainable transportation methods. For example, it selects a transportation method that takes into account the entire process from the supply of raw materials to the delivery of products. The generation AI in the Eco-Transportation Proposal Department also analyzes the environmental impact of each stage of the supply chain and proposes the most environmentally friendly transportation method. For example, it optimizes the entire process from manufacturing to delivery. The generation AI in the Eco-Transportation Proposal Department also analyzes risk data for the supply chain and proposes sustainable transportation methods that take risks into account. For example, it selects a transportation method that takes natural disasters and political risks into account. This makes it possible to analyze the environmental impact of the entire supply chain and propose sustainable transportation methods.

[0080] The eco-transportation proposal unit can use the emotion estimation function to estimate employees' environmental awareness and propose an environmental education program based on that. For example, the eco-transportation proposal unit can use the emotion estimation function to monitor employees' environmental awareness in real time and propose an environmental education program if their environmental awareness is low. For example, the eco-transportation proposal unit can provide training that explains the importance of environmental protection. The eco-transportation proposal unit can also customize the content of the environmental education program based on employees' environmental awareness. For example, it can provide a program that teaches advanced environmental protection techniques to employees with high environmental awareness. The eco-transportation proposal unit can also use the emotion estimation function to propose measures to improve employees' environmental awareness and reflect the results in the environmental education program. For example, it can provide incentives to improve environmental awareness. This makes it possible to propose an environmental education program that takes employees' environmental awareness into consideration.

[0081] The traffic information analysis unit uses IoT sensors to collect vehicle fuel efficiency data in real time, and the generation AI can then use that data to suggest optimal driving methods. For example, the traffic information analysis unit uses IoT sensors to collect vehicle fuel efficiency data in real time, and the generation AI can then use that data to suggest eco-driving methods. For example, it can suggest driving methods that avoid sudden acceleration and braking. The traffic information analysis unit also uses IoT sensors to analyze vehicle fuel efficiency data and suggest fuel-efficient routes. For example, it can select routes that are flat and have less traffic congestion. The traffic information analysis unit also uses IoT sensors to collect vehicle fuel efficiency data, and the generation AI can then use that data to suggest vehicle maintenance. For example, it can suggest tire pressure and when to change engine oil. This makes it possible to suggest optimal driving methods based on vehicle fuel efficiency data.

[0082] The traffic information analysis unit uses IoT sensors to monitor road conditions in real time, and the generation AI can use this information to generate the optimal route. For example, the traffic information analysis unit uses IoT sensors to monitor road conditions in real time, and the generation AI uses this information to generate the optimal route. For example, it proposes a route that takes into account road unevenness and construction information. The traffic information analysis unit also uses IoT sensors to analyze road conditions and propose routes with a low risk of traffic accidents. For example, it selects a route that avoids slippery roads and areas with poor visibility. The traffic information analysis unit also uses IoT sensors to collect road condition information in real time, and the generation AI uses this information to propose road maintenance. For example, it identifies damaged areas on the road and proposes areas that require repair. This makes it possible to monitor road conditions in real time and generate the optimal route.

[0083] The traffic information analysis unit can use the emotion estimation function to estimate the driver's fatigue level and suggest a break. For example, the traffic information analysis unit uses the emotion estimation function to monitor the driver's fatigue level in real time and suggest a break if fatigue is accumulating. For example, it generates a route that includes rest points. The traffic information analysis unit also makes suggestions to adjust driving time based on the driver's fatigue level. For example, it sets appropriate rest times to avoid long driving times. The traffic information analysis unit also uses the emotion estimation function to suggest relaxation content to reduce the driver's fatigue level. For example, it suggests relaxing music or stretching exercises. This makes it possible to suggest appropriate breaks taking into account the driver's fatigue level.

[0084] The inventory prediction unit uses IoT sensors to monitor the temperature and humidity in the warehouse, and the generation AI can use this information to perform optimal inventory management. For example, the inventory prediction unit uses IoT sensors to monitor the temperature and humidity in the warehouse in real time, and the generation AI uses this information to perform optimal inventory management. For example, it adjusts the temperature and humidity to keep them within an appropriate range. The inventory prediction unit also uses IoT sensors to analyze environmental data in the warehouse and makes suggestions to reduce the risk of inventory deterioration. For example, it activates the cooling system when the temperature or humidity is high. The inventory prediction unit also uses IoT sensors to monitor the temperature and humidity in the warehouse, and the generation AI uses this information to optimize inventory placement. For example, it places products that are sensitive to temperature and humidity in appropriate locations. This allows the temperature and humidity in the warehouse to be monitored and optimal inventory management to be performed.

[0085] The inventory prediction unit uses IoT sensors to collect vehicle maintenance data, and the generation AI can then suggest preventive maintenance based on that data. For example, the inventory prediction unit uses IoT sensors to collect vehicle maintenance data in real time, and the generation AI can then suggest preventive maintenance based on that data. For example, it can detect engine abnormalities and suggest early repairs. The inventory prediction unit also uses IoT sensors to analyze vehicle operation data and make suggestions to optimize the timing of maintenance. For example, it can set maintenance times based on mileage and usage conditions. The inventory prediction unit also uses IoT sensors to collect vehicle maintenance data, and the generation AI can then use that data to create a maintenance plan. For example, it can suggest a schedule for efficiently performing maintenance on multiple vehicles. This makes it possible to suggest preventive maintenance based on vehicle maintenance data.

[0086] The inventory prediction unit can use the emotion estimation function to estimate the customer's level of satisfaction at the time of receipt and adjust the delivery plan based on that. For example, the inventory prediction unit uses the emotion estimation function to monitor the customer's level of satisfaction at the time of receipt in real time and adjust the delivery plan if satisfaction is low. For example, it proposes redelivery or a change in time slot. The inventory prediction unit also identifies areas for improvement in the delivery plan based on customer satisfaction. For example, it generates a route that avoids areas or time slots with low satisfaction. The inventory prediction unit also uses the emotion estimation function to propose measures to improve customer satisfaction and reflects the results in the delivery plan. For example, it provides flexible delivery options according to customer requests. This makes it possible to adjust the delivery plan taking into account the customer's level of satisfaction at the time of receipt.

[0087] The eco-transportation suggestion unit can monitor carbon dioxide emissions in real time and suggest the optimal transportation method. For example, the generation AI in the eco-transportation suggestion unit monitors the carbon dioxide emissions of vehicles in real time and suggests transportation methods with low emissions. For example, it prioritizes routes that use electric or hybrid vehicles. The generation AI in the eco-transportation suggestion unit also analyzes the carbon dioxide emissions of transportation routes and suggests the most environmentally friendly route. For example, it selects routes that avoid traffic congestion or short routes. The generation AI in the eco-transportation suggestion unit also monitors carbon dioxide emissions in real time and adjusts the transportation method if emissions are high. For example, it sends a notification recommending eco-driving. This makes it possible to monitor carbon dioxide emissions in real time and suggest the optimal transportation method.

[0088] The eco-transportation suggestion unit can analyze energy consumption data and suggest energy-efficient transportation methods. For example, the generation AI in the eco-transportation suggestion unit analyzes vehicle energy consumption data and suggests energy-efficient transportation methods. For example, it recommends fuel-efficient vehicles and eco-driving. The generation AI in the eco-transportation suggestion unit also analyzes energy consumption data for transportation routes and suggests the most energy-efficient route. For example, it selects a route on flat roads or with less traffic congestion. The generation AI in the eco-transportation suggestion unit also monitors energy consumption data in real time and adjusts the transportation method if energy consumption is high. For example, it sends a notification recommending an energy-efficient driving method. This makes it possible to analyze energy consumption data and suggest energy-efficient transportation methods.

[0089] The eco-transportation suggestion unit can use the emotion estimation function to estimate the customer's environmental awareness and make eco-friendly suggestions based on that. The eco-transportation suggestion unit, for example, uses the emotion estimation function to monitor the customer's environmental awareness in real time and suggest eco-friendly transportation methods if the customer is highly environmentally conscious. For example, it selects a route that uses an electric vehicle or bicycle. The eco-transportation suggestion unit also presents options for eco-friendly transportation methods based on the customer's environmental awareness. For example, it preferentially suggests eco-friendly transportation methods to customers who are highly environmentally conscious. The eco-transportation suggestion unit also uses the emotion estimation function to provide information to improve the customer's environmental awareness. For example, it sends a notification explaining the benefits of eco-friendly transportation methods. In this way, the emotion estimation function can be used to make eco-friendly suggestions that take the customer's environmental awareness into consideration.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The delivery management system can further include a health management unit that monitors the driver's health condition. For example, the health management unit monitors the driver's heart rate and body temperature in real time and issues an alert if an abnormality is detected. The health management unit can also suggest rest and exercise based on the driver's health condition. For example, it can suggest appropriate rest times when driving for long periods of time. The health management unit can also analyze the driver's health data and provide advice to improve their health. For example, it can provide advice on diet and exercise. This helps maintain the driver's health and ensure efficient delivery.

[0092] The delivery management system can further include a customer analysis unit that analyzes customer purchase history. The customer analysis unit, for example, analyzes a customer's past purchase history and predicts their next purchase. The customer analysis unit can also suggest individual promotions based on the customer's purchasing patterns. For example, for a customer who frequently purchases a particular product, it can suggest a promotion related to that product. The customer analysis unit can also optimize inventory management based on the customer's purchase history. For example, it can adjust inventory so that a particular product does not run out. This makes it possible to utilize customer purchase history to achieve efficient inventory management and promotions.

[0093] The delivery management system can further include a maintenance management unit that manages the maintenance of delivery vehicles. The maintenance management unit, for example, monitors the vehicle's mileage and usage status in real time to optimize the timing of maintenance. The maintenance management unit can also detect vehicle abnormalities early and suggest preventive maintenance. For example, if an engine abnormality is detected, it can suggest early repair. The maintenance management unit can also manage the vehicle's maintenance history and create an efficient maintenance plan. For example, it can suggest a schedule for efficiently performing maintenance on multiple vehicles. This allows for optimizing vehicle maintenance and achieving efficient delivery.

[0094] The delivery management system can further include a fuel efficiency management unit that optimizes the fuel efficiency of delivery vehicles. The fuel efficiency management unit, for example, monitors the vehicle's fuel efficiency data in real time and suggests fuel-efficient driving methods. The fuel efficiency management unit can also analyze the vehicle's fuel efficiency data and suggest fuel-efficient routes. For example, it can select routes on flat roads or with less traffic congestion. The fuel efficiency management unit can also make vehicle maintenance suggestions based on the vehicle's fuel efficiency data. For example, it can suggest tire pressure and engine oil change times. This optimizes the vehicle's fuel efficiency and enables efficient delivery.

[0095] The delivery management system can further include a schedule management unit that optimizes the operation schedules of delivery vehicles. The schedule management unit, for example, monitors the operation schedules of delivery vehicles in real time and proposes efficient schedules. The schedule management unit can also analyze the operation data of delivery vehicles and generate optimal operation schedules. For example, when multiple vehicles cover the same area, it proposes a schedule that avoids overlap. The schedule management unit can also create efficient delivery plans based on the operation schedules of delivery vehicles. For example, when the recipient of the package is close, it proposes a schedule that allows for efficient delivery. This allows the operation schedules of delivery vehicles to be optimized and efficient delivery to be achieved.

[0096] The delivery management system can further use emotion estimation functions to estimate customer satisfaction and reflect this in delivery plans. For example, it can monitor customer satisfaction in real time and adjust delivery plans if satisfaction is low. For example, it can suggest redelivery or changing the time slot. It can also identify areas for improvement in delivery plans based on customer satisfaction. For example, it can generate routes that avoid areas or time slots with low satisfaction. It can also propose measures to improve customer satisfaction and reflect the results in delivery plans. For example, it can provide flexible delivery options according to customer requests. This makes it possible to adjust delivery plans that take customer satisfaction into account.

[0097] The delivery management system can further use emotion estimation functions to estimate the driver's stress level and suggest routes that reduce stress. For example, it can monitor the driver's stress level in real time and suggest a relaxing route if the driver's stress level is high. For example, it can select a route with good scenery or less congestion. It can also suggest routes that include rest stops based on the driver's stress level. For example, if the driver is tired, it can generate a route that includes appropriate rest stops. It can also suggest music or relaxation content to reduce the driver's stress level. For example, it can play relaxing music if the driver's stress level is high. This makes it possible to suggest routes that reduce the driver's stress.

[0098] The delivery management system can also use emotion estimation functions to estimate employee motivation and reflect this in inventory management. For example, it can monitor employee motivation in real time and perform efficient inventory management when motivation is high. It can also identify areas for improvement in inventory management based on employee motivation. For example, it can review the inventory management process when motivation is low. It can also propose measures to improve employee motivation and reflect the results in inventory management. For example, it can suggest training or incentives to improve motivation. This makes it possible to perform inventory management that takes employee motivation into account.

[0099] The delivery management system can further use the emotion estimation function to estimate a customer's environmental awareness and suggest eco-friendly delivery methods based on that. For example, it can monitor a customer's environmental awareness in real time and suggest eco-friendly delivery methods if the customer is highly environmentally conscious. For example, it can select a route that uses electric vehicles or bicycles. It can also present options for eco-friendly delivery methods based on the customer's environmental awareness. For example, it can preferentially suggest eco-friendly delivery methods to customers who are highly environmentally conscious. It can also provide information to improve customers' environmental awareness. For example, it can send a notification explaining the benefits of eco-friendly delivery methods. This makes it possible to suggest eco-friendly delivery methods that take the customer's environmental awareness into consideration.

[0100] The delivery management system can further use emotion estimation functions to estimate the health status of employees and suggest health-conscious transportation methods. For example, it can monitor employees' health status in real time and suggest routes that include breaks if their health is poor. It can also present options for transportation methods based on the employee's health status. For example, it can suggest long-distance transportation if their health is good, and short-distance transportation if their health is poor. It can also suggest measures to improve employees' health and reflect the results in the transportation method. For example, it can suggest training or breaks to improve their health. This makes it possible to suggest transportation methods that take employees' health status into consideration.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The traffic information analysis unit analyzes real-time traffic information. For example, it analyzes traffic congestion and accident information and selects the most efficient route. The traffic information analysis unit also collects traffic information, and the generation AI uses it to generate the optimal route. Step 2: The route generation unit generates the optimal delivery route based on the traffic information analyzed by the traffic information analysis unit. For example, the generation AI proposes the shortest route based on the traffic information. The route generation unit also uses an algorithm to generate the optimal delivery route based on the traffic information. Step 3: The inventory forecasting unit analyzes inventory levels and demand forecasts. For example, a generative AI analyzes inventory data and demand forecast data to create an optimal delivery plan. The inventory forecasting unit also uses an algorithm to create an optimal delivery plan based on inventory levels and demand forecasts. Step 4: The delivery planning unit creates an optimal delivery plan based on the inventory levels and demand forecasts analyzed by the inventory forecasting unit. For example, a generation AI generates an optimal delivery plan based on inventory data and demand forecast data. The delivery planning unit also uses an algorithm to create an optimal delivery plan based on inventory levels and demand forecasts. Step 5: The Eco-Friendly Transportation Proposal Unit proposes the most eco-friendly transportation method, taking into account the cost, efficiency, and environmental impact of each method. For example, the Generative AI analyzes the transportation options and environmental impact data to propose the most eco-friendly transportation method. The Eco-Friendly Transportation Proposal Unit also uses an algorithm to propose the optimal transportation method based on the cost, efficiency, and environmental impact of each method.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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.

[0130] 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.

[0131] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] 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. [Explanation of symbols]

[0170] 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. Traffic Information Analysis Department that analyzes real-time traffic information; a route generation unit that generates an optimal delivery route based on the traffic information analyzed by the traffic information analysis unit; an inventory forecasting department that analyzes inventory levels and demand forecasts; a delivery planning unit that creates an optimal delivery plan based on the inventory level analyzed by the inventory forecasting unit and the demand forecast; An eco-friendly transportation proposal department that proposes the most eco-friendly transportation method in consideration of the cost, efficiency, and environmental impact of the transportation method. A system characterized by:

2. The traffic information analysis unit In addition to the traffic information, weather data and event information are also analyzed to generate the optimal route.

2. The system of claim 1.

3. The traffic information analysis unit Learns past traffic patterns and predicts future traffic conditions to generate routes 2. The system of claim 1.

4. The traffic information analysis unit Estimates the driver's stress level and suggests routes that reduce stress 2. The system of claim 1.

5. The inventory prediction unit Make demand forecasts taking into account past sales data and seasonality 2. The system of claim 1.

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