System

Abstract

An object of a system according to an embodiment is to maximize the efficiency of physical distribution.SOLUTION: A system according to an embodiment includes a baggage arrangement generation unit, an operation route generation unit, and a loading / unloading work generation unit. The cargo arrangement generation unit generates an optimal arrangement plan of the cargo based on information on a size, a shape, a weight, and a delivery destination of the cargo. The travel route generation unit optimizes the travel route based on the information on the address of the delivery destination, the traffic situation, and the congestion degree of the road. The loading / unloading work generation section generates a plan for improving the efficiency of the loading / unloading work based on the arrangement information of the loads, the order of loading / unloading, and the information of the work time.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] Conventional technology has not sufficiently improved the efficiency of logistics, and there is room for improvement, especially for small and medium-sized truck operators.

[0005] The system according to the embodiment aims to maximize the efficiency of logistics. [Means for solving the problem]

[0006] The system according to the embodiment includes a luggage placement generation unit, a travel route generation unit, and a loading and unloading work generation unit. The luggage placement generation unit generates an optimal luggage placement plan based on information on the size, shape, weight, and delivery destination of the luggage. The travel route generation unit optimizes the travel route based on information on the delivery destination address, traffic conditions, and road congestion. The loading and unloading work generation unit generates a plan to streamline loading and unloading work based on information on luggage placement information, loading and unloading order, and work time. [Effects of the Invention]

[0007] The system according to the embodiment can maximize the efficiency of logistics. [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 logistics efficiency improvement system according to an embodiment of the present invention is a system that uses generation AI to efficiently fill truck beds and improve logistics efficiency to the ultimate level. As a result, the logistics efficiency improvement system can efficiently fill truck beds and improve logistics efficiency to the ultimate level.

[0029] A logistics efficiency improvement system according to an embodiment includes a luggage placement generation unit, a travel route generation unit, and a loading / unloading operation generation unit. The luggage placement generation unit generates an optimal luggage placement plan based on information about the size, shape, weight, and delivery destination of the luggage. For example, the generation AI proposes an optimal placement based on the size and shape of the luggage. The generation AI also proposes a placement that maintains truck balance by taking into account the weight of the luggage. The generation AI also proposes a placement that enables efficient loading and unloading of luggage based on information about the delivery destination. The travel route generation unit optimizes the travel route based on information about the delivery destination address, traffic conditions, and road congestion. For example, the generation AI proposes an optimal route based on real-time traffic information. The generation AI also analyzes past traffic patterns to propose a route that avoids traffic jams. The generation AI also proposes a route that allows delivery in the shortest time, taking into account road congestion. The loading / unloading operation generation unit generates a plan to streamline loading and unloading operations based on information about luggage placement, loading and unloading order, and work time. For example, the generation AI proposes an optimal loading and unloading order based on the luggage placement information. The generation AI also proposes efficient loading and unloading operations by taking into account work time. The generation AI also optimizes the loading and unloading order to reduce the burden on workers. As a result, the logistics efficiency system according to the embodiment enables optimal placement of cargo, optimization of travel routes, and efficiency improvement of loading and unloading work. For example, optimal placement of cargo improves truck loading efficiency, and optimization of travel routes shortens delivery time. Furthermore, efficiency improvement of loading and unloading work shortens work time, improving overall logistics efficiency.

[0030] The luggage placement generation unit can generate a placement plan that minimizes the effects of vibration or shock based on the shape or material of the luggage. For example, the generation AI generates a placement plan that minimizes the effects of vibration and shock based on the shape and material of the luggage. For example, fragile glass products are placed in areas with less vibration, and heavy metal products are placed in the center of the truck. The generation AI also considers the placement of cushioning material to absorb vibration and shock depending on the material of the luggage. For example, electronic devices are wrapped in cushioning material to minimize the effects of vibration. The generation AI also suggests the optimal stacking method based on the shape of the luggage. For example, for box-shaped luggage, items with wider bases are placed at the bottom to maintain stability, and lighter items are stacked on top. This minimizes the effects of vibration and shock based on the shape and material of the luggage.

[0031] The luggage placement generation unit can place luggage that requires temperature control in the optimal location based on temperature sensor data. For example, the generation AI places luggage that requires temperature control in the optimal location based on temperature sensor data. For example, refrigerated items are placed in a location where cold air can easily reach them, minimizing temperature changes. The generation AI also analyzes temperature sensor data in real time and dynamically adjusts the placement of luggage that requires temperature control. For example, if the outside temperature rises, refrigerated items are moved to the center of the truck. The generation AI also proposes a plan to place insulation around luggage that requires temperature control based on temperature sensor data. For example, frozen foods are wrapped in insulation to prevent temperature changes. This allows luggage that requires temperature control to be placed in the optimal location.

[0032] The cargo placement generation unit can generate plans that optimize not only cargo placement, but also air conditioning and humidity control inside the truck. For example, the generation AI generates a plan that optimizes cargo placement as well as air conditioning and humidity control inside the truck. For example, humidity-sensitive cargo is placed in a low-humidity location and the air conditioning is adjusted. The generation AI also optimizes the settings of the air conditioning system according to the cargo placement. For example, if there are many refrigerated items, the air conditioning is adjusted so that cool air is distributed evenly. The generation AI also suggests the placement of cargo that requires humidity control based on data from a humidity sensor. For example, paper products are placed in a low-humidity location to prevent moisture. This makes it possible to optimize the air conditioning and humidity control inside the truck.

[0033] The cargo placement generation unit can generate cargo placement plans that can be applied to other means of transportation. For example, the generation AI generates cargo placement plans that can be applied not only to trucks but also to trains and ships. For example, in the case of rail transport, cargo that is resistant to vibrations is placed at the bottom. The generation AI also takes multimodal transport into consideration and proposes the optimal cargo placement plan for each means of transportation. For example, in ship transport, heavy cargo is placed at the bottom to keep the center of gravity low. The generation AI also generates placement plans to streamline cargo transfer between different means of transportation. For example, it places cargo so that transfer from truck to train can be done smoothly. This makes it possible to generate cargo placement plans that can be applied to other means of transportation.

[0034] In addition to optimizing the driving route, the driving route generation unit can also propose a route that minimizes fuel consumption. For example, the generation AI in the driving route generation unit not only optimizes the driving route but also proposes a route that minimizes fuel consumption. For example, it may prioritize flat roads to reduce fuel consumption. The generation AI also proposes the optimal driving route based on fuel consumption data. For example, it may select a route with fewer traffic lights to reduce fuel waste. The generation AI also monitors fuel consumption in real time and dynamically adjusts the optimal driving route. For example, it may select a route that avoids traffic jams to minimize fuel consumption. This makes it possible to propose a driving route that minimizes fuel consumption.

[0035] The travel route generation unit can collect traffic accident or construction information in real time and dynamically change the route based on that information. In the travel route generation unit, for example, the generation AI collects traffic accident and construction information in real time and dynamically changes the travel route based on that information. For example, if an accident occurs, it will propose a detour route. The generation AI will also propose the optimal travel route based on the construction information. For example, it will select a route that avoids roads under construction to prevent delays. The generation AI will also analyze traffic accident and construction information in real time and dynamically adjust the optimal travel route. For example, if congestion occurs, it will propose an alternative route. This makes it possible to dynamically change the route based on traffic accident and construction information.

[0036] In addition to optimizing the driving route, the driving route generation unit can also optimize the truck maintenance schedule. In addition to optimizing the driving route, the generation AI can also optimize the truck maintenance schedule. For example, it can suggest maintenance facilities along the driving route. The generation AI can also suggest optimal driving routes and maintenance schedules based on maintenance data. For example, it can set routes that coincide with the timing of regular inspections. The generation AI can also monitor the condition of trucks in real time and suggest optimal maintenance schedules. For example, if an abnormality is detected, it can suggest the nearest maintenance facility. This can also optimize the truck maintenance schedule.

[0037] The operation route generation unit promotes cooperation between different transport companies and can generate routes that allow multiple trucks to work together efficiently. In the operation route generation unit, for example, the generation AI promotes cooperation between different transport companies and generates routes that allow multiple trucks to work together efficiently. For example, it suggests transshipment points for cargo. The generation AI also proposes optimal cooperation routes based on data between transport companies. For example, it sets routes that allow multiple trucks to deliver cargo efficiently. The generation AI also dynamically adjusts routes to promote cooperation between different transport companies. For example, it suggests routes that allow for smooth delivery of cargo. This promotes cooperation between different transport companies and can generate routes that allow multiple trucks to work together efficiently.

[0038] The loading and unloading work generation unit can propose the optimal placement of tools and equipment required for loading and unloading work. In the loading and unloading work generation unit, for example, the generation AI proposes the optimal placement of tools and equipment required for loading and unloading work. For example, it optimizes the placement of forklifts and pallets. The generation AI also generates an optimal placement plan based on data on tools and equipment. For example, it places tools close to each other to improve work efficiency. The generation AI also proposes the placement of tools and equipment to minimize worker movement lines. For example, it places frequently used tools within reach of workers. This makes it possible to propose the optimal placement of tools and equipment required for loading and unloading work.

[0039] The loading and unloading work generation unit can optimize the loading and unloading order of luggage to minimize worker movement lines. In the loading and unloading work generation unit, for example, the generation AI optimizes the loading and unloading order of luggage to minimize worker movement lines. For example, loading and unloading luggage starting from the closest luggage. The generation AI also proposes the optimal loading and unloading order based on worker movement line data. For example, it arranges luggage so that workers do not make unnecessary movements. The generation AI also monitors worker movement lines in real time and dynamically adjusts the optimal loading and unloading order. For example, it proposes an efficient order to reduce worker fatigue. This makes it possible to optimize the loading and unloading order of luggage to minimize worker movement lines.

[0040] The loading and unloading work generation unit can automate the documents and procedures required for loading and unloading work. For example, the generation AI automates the documents and procedures required for loading and unloading work. For example, it automatically generates an unloading list and confirmation document. The generation AI also proposes the optimal automation plan based on document and procedure data. For example, it prepares the necessary documents in advance, reducing the workload of workers. The generation AI also monitors the progress of loading and unloading work in real time and automatically updates the necessary documents and procedures. For example, it automatically generates a confirmation document when the work is completed. This makes it possible to automate the documents and procedures required for loading and unloading work.

[0041] The loading and unloading work generation unit can link the loading and unloading work of luggage with inventory management and quality checks. In the loading and unloading work generation unit, for example, the generation AI generates a plan to link the loading and unloading work of luggage with inventory management and quality checks. For example, it updates inventory data simultaneously with loading and unloading. The generation AI also proposes an optimal linkage plan based on other work data. For example, it performs quality checks simultaneously with loading and unloading work. The generation AI also monitors the progress of the loading and unloading work in real time and dynamically adjusts the plan to link it with other work. For example, it links with an inventory management system to automatically update data. This allows the loading and unloading work to be linked with other work.

[0042] The operation route generation unit monitors not only the operation status of trucks but also the health status of drivers, and is able to propose optimal operation plans. For example, the generation AI in the operation route generation unit monitors the operation status of trucks and the health status of drivers, and proposes optimal operation plans. For example, it adjusts working hours according to the driver's health status. The generation AI also proposes optimal operation plans based on health data. For example, it assigns long driving hours to drivers in good health. The generation AI also monitors the driver's health status in real time, and dynamically adjusts the optimal operation plan. For example, it suggests taking a break if the driver's health status deteriorates. In this way, the operation status of trucks and the driver's health status can be monitored, and optimal operation plans can be proposed.

[0043] The operation route generation unit can generate an operation plan that minimizes fuel consumption and maintenance costs. In the operation route generation unit, for example, the generation AI generates an operation plan that minimizes fuel consumption and maintenance costs. For example, it selects a route with good fuel efficiency to reduce fuel consumption. The generation AI also proposes an optimal operation plan based on maintenance data. For example, it performs inspections when maintenance costs are low. The generation AI also monitors fuel consumption and maintenance costs in real time and dynamically adjusts the optimal operation plan. For example, if fuel prices rise, it proposes a route with good fuel efficiency. This makes it possible to generate an operation plan that minimizes fuel consumption and maintenance costs.

[0044] The operation route generation unit can analyze the management data of truck operators and make specific proposals for improving management. In the operation route generation unit, for example, the generation AI analyzes the management data of truck operators and makes specific proposals for improving management. For example, it proposes an operation plan for reducing costs. The generation AI also proposes an optimal management improvement plan based on the management data. For example, it selects a highly profitable route to maximize profits. The generation AI also monitors the management data in real time and dynamically adjusts the plan for improving management. For example, if profits decline, it proposes cost-cutting measures. This makes it possible to analyze the management data of truck operators and make specific proposals for improving management.

[0045] The operation route generation unit can promote cooperation between different truck operators and propose resource sharing and joint delivery. In the operation route generation unit, for example, the generation AI promotes cooperation between different truck operators and proposes resource sharing and joint delivery. For example, multiple operators jointly set delivery routes. The generation AI also proposes an optimal cooperation plan based on data between the operators. For example, it proposes cost-cutting measures through resource sharing. The generation AI also dynamically adjusts plans to promote cooperation between different truck operators. For example, it proposes routes that maximize the efficiency of joint delivery. This promotes cooperation between different truck operators and proposes resource sharing and joint delivery.

[0046] The operation route generation unit can generate plans that not only improve logistics efficiency but also minimize environmental impact. For example, the generation AI in the operation route generation unit generates plans that not only improve logistics efficiency but also minimize environmental impact. For example, it selects a route with good fuel efficiency and reduces CO2 emissions. The generation AI also proposes optimal logistics plans based on environmental data. For example, it recommends eco-driving and reduces fuel consumption. The generation AI also monitors environmental impact in real time and dynamically adjusts the optimal logistics plan. For example, it selects an environmentally friendly route and minimizes environmental impact. This makes it possible to generate plans that not only improve logistics efficiency but also minimize environmental impact.

[0047] The operation route generation unit can analyze logistics data in real time and instantly update the efficiency plan. In the operation route generation unit, for example, the generation AI analyzes logistics data in real time and instantly updates the efficiency plan. For example, it proposes the optimal route depending on traffic conditions. The generation AI also proposes the optimal efficiency plan based on the logistics data. For example, it dynamically adjusts the route depending on changes in delivery destinations. The generation AI also monitors logistics data in real time and instantly updates the efficiency plan. For example, it proposes a detour route based on traffic congestion information. This makes it possible to analyze logistics data in real time and instantly update the efficiency plan.

[0048] The operation route generation unit can generate plans that maximize customer satisfaction in addition to improving logistics efficiency. In the operation route generation unit, for example, the generation AI generates plans that maximize customer satisfaction in addition to improving logistics efficiency. For example, it adjusts delivery times to meet customer requests. The generation AI also proposes optimal logistics plans based on customer data. For example, it adjusts delivery routes to match the customer's desired times. The generation AI also monitors customer satisfaction in real time and dynamically adjusts the optimal logistics plan. For example, it changes delivery times based on customer feedback. This makes it possible to generate plans that maximize customer satisfaction in addition to improving logistics efficiency.

[0049] The operation route generation unit promotes data sharing between different logistics companies, thereby improving overall logistics efficiency. In the operation route generation unit, for example, the generation AI promotes data sharing between different logistics companies, thereby improving overall logistics efficiency. For example, it proposes the sharing of delivery routes. The generation AI also proposes an optimal logistics plan based on data between companies. For example, it proposes cost-cutting measures through joint delivery. The generation AI also dynamically adjusts plans to promote data sharing between different logistics companies. For example, it shares data in real time and proposes efficient delivery routes. This promotes data sharing between different logistics companies, thereby improving overall logistics efficiency.

[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 luggage placement generation unit generates an optimal luggage placement plan based on information about the luggage's size, shape, weight, and delivery destination. For example, the generation AI proposes an optimal placement based on the luggage's size and shape. The generation AI also proposes a placement that maintains truck balance, taking into account the luggage's weight. The generation AI also proposes a placement that enables efficient loading and unloading of luggage based on delivery destination information. The operation route generation unit optimizes the operation route based on information about the destination address, traffic conditions, and road congestion. For example, the generation AI proposes an optimal route based on real-time traffic information. The generation AI also analyzes past traffic patterns to propose a route that avoids traffic jams. The generation AI also proposes a route that allows delivery in the shortest time, taking into account road congestion. The loading and unloading work generation unit generates a plan that streamlines loading and unloading work based on information about luggage placement, the loading and unloading order, and work time. For example, the generation AI proposes an optimal loading and unloading order based on luggage placement information. The generation AI also proposes efficient loading and unloading work, taking into account work time. The generation AI also optimizes the loading and unloading order to reduce the burden on workers. As a result, the logistics efficiency improvement system according to the embodiment enables optimal placement of cargo, optimization of transportation routes, and efficiency improvement of loading and unloading work. For example, optimal placement of cargo improves truck loading efficiency, and optimization of transportation routes reduces delivery time. Furthermore, efficient loading and unloading reduces work time, improving overall logistics efficiency.

[0052] The luggage placement generation unit can generate a placement plan that minimizes the impact of vibration or shock based on the shape or material of the luggage. For example, the generation AI generates a placement plan that minimizes the impact of vibration or shock based on the shape and material of the luggage. For example, fragile glass products are placed in areas with less vibration, and heavy metal products are placed in the center of the truck. The generation AI also considers the placement of cushioning material to absorb vibration and shock depending on the material of the luggage. For example, electronic devices are wrapped in cushioning material to minimize the impact of vibration. The generation AI also suggests the optimal stacking method based on the shape of the luggage. For example, for box-shaped luggage, items with wider bases are placed at the bottom to maintain stability, and lighter items are stacked on top. This minimizes the impact of vibration and shock based on the shape and material of the luggage.

[0053] The luggage placement generation unit can place luggage that requires temperature control in the optimal location based on temperature sensor data. For example, the generation AI places luggage that requires temperature control in the optimal location based on temperature sensor data. For example, refrigerated items are placed in locations where cool air can easily reach them, minimizing temperature changes. The generation AI also analyzes temperature sensor data in real time and dynamically adjusts the placement of luggage that requires temperature control. For example, if the outside temperature rises, refrigerated items are moved to the center of the truck. The generation AI also proposes a plan to place insulation around luggage that requires temperature control based on temperature sensor data. For example, frozen foods are wrapped in insulation to prevent temperature changes. This allows luggage that requires temperature control to be placed in the optimal location.

[0054] The cargo placement generation unit can generate plans that optimize not only cargo placement, but also air conditioning and humidity control inside the truck. For example, the generation AI generates a plan that optimizes cargo placement as well as air conditioning and humidity control inside the truck. For example, humidity-sensitive cargo is placed in a low-humidity location and the air conditioning is adjusted accordingly. The generation AI also optimizes the settings of the air conditioning system according to the cargo placement. For example, if there are many refrigerated items, the air conditioning is adjusted so that cool air is distributed evenly. The generation AI also suggests placement of cargo that requires humidity control based on data from humidity sensors. For example, paper products are placed in a low-humidity location to prevent moisture. This makes it possible to optimize the air conditioning and humidity control inside the truck.

[0055] The cargo placement generation unit can generate cargo placement plans that can be applied to other means of transportation. For example, the generation AI generates cargo placement plans that can be applied not only to trucks but also to trains and ships. For example, in the case of rail transport, cargo that is resistant to vibrations is placed at the bottom. The generation AI also takes multimodal transport into consideration and proposes the optimal cargo placement plan for each means of transportation. For example, in ship transport, heavy cargo is placed at the bottom to keep the center of gravity low. The generation AI also generates placement plans to streamline cargo transfers between different means of transportation. For example, it places cargo so that transfers from truck to train can be done smoothly. This makes it possible to generate cargo placement plans that can be applied to other means of transportation.

[0056] In addition to optimizing the driving route, the driving route generation unit can also propose routes that minimize fuel consumption. For example, in addition to optimizing the driving route, the generation AI can also propose routes that minimize fuel consumption. For example, it can prioritize flat roads to reduce fuel consumption. The generation AI can also propose optimal driving routes based on fuel consumption data. For example, it can select routes with fewer traffic lights to reduce fuel waste. The generation AI can also monitor fuel consumption in real time and dynamically adjust the optimal driving route. For example, it can select routes that avoid traffic jams to minimize fuel consumption. This makes it possible to propose driving routes that minimize fuel consumption.

[0057] The route generation unit can collect traffic accident or construction information in real time and dynamically change the route based on that information. For example, the generation AI collects traffic accident and construction information in real time and dynamically changes the route based on that information. For example, if an accident occurs, it will suggest a detour route. The generation AI also proposes the optimal route based on the construction information. For example, it will choose a route that avoids roads under construction to prevent delays. The generation AI also analyzes traffic accident and construction information in real time and dynamically adjusts the optimal route. For example, if congestion occurs, it will suggest a different route. This allows the route to be dynamically changed based on traffic accident and construction information.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The cargo placement generation unit generates an optimal cargo placement plan based on the size, shape, weight, and delivery destination of the cargo. For example, the generation AI proposes an optimal placement based on the size and shape of the cargo, and proposes a placement that maintains truck balance by taking into account the weight of the cargo. It also proposes a placement that allows for efficient loading and unloading of cargo based on delivery destination information. Step 2: The route generation unit optimizes the route based on information about the delivery destination address, traffic conditions, and road congestion. For example, the generation AI proposes the optimal route based on real-time traffic information, analyzes past traffic patterns to suggest routes that avoid congestion, and also proposes the shortest delivery route, taking road congestion into account. Step 3: The loading and unloading work generation unit generates a plan to streamline loading and unloading work based on information on the placement of luggage, the loading and unloading order, and the work time. For example, the generation AI proposes the optimal loading and unloading order based on the luggage placement information, and proposes efficient loading and unloading work taking into account the work time. It also optimizes the loading and unloading order to reduce the burden on workers.

[0060] (Example 2) A logistics efficiency improvement system according to an embodiment of the present invention is a system that uses generation AI to efficiently fill truck beds and improve logistics efficiency to the ultimate level. As a result, the logistics efficiency improvement system can efficiently fill truck beds and improve logistics efficiency to the ultimate level.

[0061] A logistics efficiency improvement system according to an embodiment includes a luggage placement generation unit, a travel route generation unit, and a loading / unloading operation generation unit. The luggage placement generation unit generates an optimal luggage placement plan based on information about the size, shape, weight, and delivery destination of the luggage. For example, the generation AI proposes an optimal placement based on the size and shape of the luggage. The generation AI also proposes a placement that maintains truck balance by taking into account the weight of the luggage. The generation AI also proposes a placement that enables efficient loading and unloading of luggage based on information about the delivery destination. The travel route generation unit optimizes the travel route based on information about the delivery destination address, traffic conditions, and road congestion. For example, the generation AI proposes an optimal route based on real-time traffic information. The generation AI also analyzes past traffic patterns to propose a route that avoids traffic jams. The generation AI also proposes a route that allows delivery in the shortest time, taking into account road congestion. The loading / unloading operation generation unit generates a plan to streamline loading and unloading operations based on information about luggage placement, loading and unloading order, and work time. For example, the generation AI proposes an optimal loading and unloading order based on the luggage placement information. The generation AI also proposes efficient loading and unloading operations by taking into account work time. The generation AI also optimizes the loading and unloading order to reduce the burden on workers. As a result, the logistics efficiency system according to the embodiment enables optimal placement of cargo, optimization of travel routes, and efficiency improvement of loading and unloading work. For example, optimal placement of cargo improves truck loading efficiency, and optimization of travel routes shortens delivery time. Furthermore, efficiency improvement of loading and unloading work shortens work time, improving overall logistics efficiency.

[0062] The luggage placement generation unit can generate a placement plan that minimizes the effects of vibration or shock based on the shape or material of the luggage. For example, the generation AI generates a placement plan that minimizes the effects of vibration and shock based on the shape and material of the luggage. For example, fragile glass products are placed in areas with less vibration, and heavy metal products are placed in the center of the truck. The generation AI also considers the placement of cushioning material to absorb vibration and shock depending on the material of the luggage. For example, electronic devices are wrapped in cushioning material to minimize the effects of vibration. The generation AI also suggests the optimal stacking method based on the shape of the luggage. For example, for box-shaped luggage, items with wider bases are placed at the bottom to maintain stability, and lighter items are stacked on top. This minimizes the effects of vibration and shock based on the shape and material of the luggage.

[0063] The luggage placement generation unit can place luggage that requires temperature control in the optimal location based on temperature sensor data. For example, the generation AI places luggage that requires temperature control in the optimal location based on temperature sensor data. For example, refrigerated items are placed in a location where cold air can easily reach them, minimizing temperature changes. The generation AI also analyzes temperature sensor data in real time and dynamically adjusts the placement of luggage that requires temperature control. For example, if the outside temperature rises, refrigerated items are moved to the center of the truck. The generation AI also proposes a plan to place insulation around luggage that requires temperature control based on temperature sensor data. For example, frozen foods are wrapped in insulation to prevent temperature changes. This allows luggage that requires temperature control to be placed in the optimal location.

[0064] The luggage placement generation unit can use the emotion estimation function to generate a luggage placement plan based on the driver's stress level. For example, the generation AI uses the emotion estimation function to analyze the driver's stress level and generate a luggage placement plan that reduces stress. For example, it removes heavy luggage from the driver's field of view. The generation AI also monitors the driver's stress level in real time and dynamically adjusts luggage placement to reduce stress. For example, if stress increases, it suggests rearranging luggage. The generation AI also proposes a luggage placement plan to reduce driver stress based on the emotion estimation data. For example, it may place luggage so as not to obstruct the driver's view, improving driving comfort. This makes it possible to generate a luggage placement plan that takes the driver's stress level into account.

[0065] The cargo placement generation unit can generate plans that optimize not only cargo placement, but also air conditioning and humidity control inside the truck. For example, the generation AI generates a plan that optimizes cargo placement as well as air conditioning and humidity control inside the truck. For example, humidity-sensitive cargo is placed in a low-humidity location and the air conditioning is adjusted. The generation AI also optimizes the settings of the air conditioning system according to the cargo placement. For example, if there are many refrigerated items, the air conditioning is adjusted so that cool air is distributed evenly. The generation AI also suggests the placement of cargo that requires humidity control based on data from a humidity sensor. For example, paper products are placed in a low-humidity location to prevent moisture. This makes it possible to optimize the air conditioning and humidity control inside the truck.

[0066] The cargo placement generation unit can generate cargo placement plans that can be applied to other means of transportation. For example, the generation AI generates cargo placement plans that can be applied not only to trucks but also to trains and ships. For example, in the case of rail transport, cargo that is resistant to vibrations is placed at the bottom. The generation AI also takes multimodal transport into consideration and proposes the optimal cargo placement plan for each means of transportation. For example, in ship transport, heavy cargo is placed at the bottom to keep the center of gravity low. The generation AI also generates placement plans to streamline cargo transfer between different means of transportation. For example, it places cargo so that transfer from truck to train can be done smoothly. This makes it possible to generate cargo placement plans that can be applied to other means of transportation.

[0067] The package placement generation unit can use the emotion estimation function to predict the emotions of the package recipient and propose a delivery order that will most satisfy the recipient. For example, the generation AI in the package placement generation unit uses the emotion estimation function to predict the emotions of the package recipient and propose a delivery order that will most satisfy the recipient. For example, it may deliver urgent packages first. The generation AI also dynamically adjusts the delivery order based on the recipient's emotion data. For example, if the recipient is not at home, it may deliver other packages first. The generation AI also proposes a delivery order that will most satisfy the recipient based on the emotion estimation data. For example, it adjusts the delivery order to match the recipient's desired time. In this way, it is possible to predict the emotions of the package recipient and propose a delivery order that will most satisfy the recipient.

[0068] In addition to optimizing the driving route, the driving route generation unit can also propose a route that minimizes fuel consumption. For example, the generation AI in the driving route generation unit not only optimizes the driving route but also proposes a route that minimizes fuel consumption. For example, it may prioritize flat roads to reduce fuel consumption. The generation AI also proposes the optimal driving route based on fuel consumption data. For example, it may select a route with fewer traffic lights to reduce fuel waste. The generation AI also monitors fuel consumption in real time and dynamically adjusts the optimal driving route. For example, it may select a route that avoids traffic jams to minimize fuel consumption. This makes it possible to propose a driving route that minimizes fuel consumption.

[0069] The travel route generation unit can collect traffic accident or construction information in real time and dynamically change the route based on that information. In the travel route generation unit, for example, the generation AI collects traffic accident and construction information in real time and dynamically changes the travel route based on that information. For example, if an accident occurs, it will propose a detour route. The generation AI will also propose the optimal travel route based on the construction information. For example, it will select a route that avoids roads under construction to prevent delays. The generation AI will also analyze traffic accident and construction information in real time and dynamically adjust the optimal travel route. For example, if congestion occurs, it will propose an alternative route. This makes it possible to dynamically change the route based on traffic accident and construction information.

[0070] The driving route generation unit can use the emotion estimation function to generate a driving route that includes rest points based on the driver's fatigue level. For example, the generation AI in the driving route generation unit uses the emotion estimation function to analyze the driver's fatigue level and generate a driving route that includes rest points. For example, if fatigue increases, it will suggest appropriate rest locations. The generation AI also monitors the driver's fatigue level in real time and dynamically adjusts the optimal driving route that includes rest points. For example, it will suggest a break after a long drive. The generation AI also suggests a driving route that includes rest points to reduce driver fatigue based on the emotion estimation data. For example, it will set a route that includes regular breaks. This makes it possible to generate a driving route that includes rest points that takes the driver's fatigue level into consideration.

[0071] In addition to optimizing the driving route, the driving route generation unit can also optimize the truck maintenance schedule. In addition to optimizing the driving route, the generation AI can also optimize the truck maintenance schedule. For example, it can suggest maintenance facilities along the driving route. The generation AI can also suggest optimal driving routes and maintenance schedules based on maintenance data. For example, it can set routes that coincide with the timing of regular inspections. The generation AI can also monitor the condition of trucks in real time and suggest optimal maintenance schedules. For example, if an abnormality is detected, it can suggest the nearest maintenance facility. This can also optimize the truck maintenance schedule.

[0072] The operation route generation unit promotes cooperation between different transport companies and can generate routes that allow multiple trucks to work together efficiently. In the operation route generation unit, for example, the generation AI promotes cooperation between different transport companies and generates routes that allow multiple trucks to work together efficiently. For example, it suggests transshipment points for cargo. The generation AI also proposes optimal cooperation routes based on data between transport companies. For example, it sets routes that allow multiple trucks to deliver cargo efficiently. The generation AI also dynamically adjusts routes to promote cooperation between different transport companies. For example, it suggests routes that allow for smooth delivery of cargo. This promotes cooperation between different transport companies and can generate routes that allow multiple trucks to work together efficiently.

[0073] The delivery route generation unit can use the emotion estimation function to predict the emotions of the customer at the delivery destination and propose a delivery route at the time that will make the customer happiest. For example, the delivery route generation unit uses the emotion estimation function to predict the emotions of the customer at the delivery destination and proposes a delivery route at the time that will make the customer happiest. For example, it prioritizes the time when the customer is at home. The generation AI also proposes the optimal delivery time based on the customer's emotion data. For example, it adjusts the delivery route to match the time that will make the customer happiest. The generation AI also dynamically adjusts the delivery route at the time that will make the customer happiest based on the emotion estimation data. For example, it changes the route to match the customer's desired time. In this way, it is possible to predict the customer's emotions and propose a delivery route at the time that will make the customer happiest.

[0074] The loading and unloading work generation unit can propose the optimal placement of tools and equipment required for loading and unloading work. In the loading and unloading work generation unit, for example, the generation AI proposes the optimal placement of tools and equipment required for loading and unloading work. For example, it optimizes the placement of forklifts and pallets. The generation AI also generates an optimal placement plan based on data on tools and equipment. For example, it places tools close to each other to improve work efficiency. The generation AI also proposes the placement of tools and equipment to minimize worker movement lines. For example, it places frequently used tools within reach of workers. This makes it possible to propose the optimal placement of tools and equipment required for loading and unloading work.

[0075] The loading and unloading work generation unit can optimize the loading and unloading order of luggage to minimize worker movement lines. In the loading and unloading work generation unit, for example, the generation AI optimizes the loading and unloading order of luggage to minimize worker movement lines. For example, loading and unloading luggage starting from the closest luggage. The generation AI also proposes the optimal loading and unloading order based on worker movement line data. For example, it arranges luggage so that workers do not make unnecessary movements. The generation AI also monitors worker movement lines in real time and dynamically adjusts the optimal loading and unloading order. For example, it proposes an efficient order to reduce worker fatigue. This makes it possible to optimize the loading and unloading order of luggage to minimize worker movement lines.

[0076] The loading and unloading work generation unit can use the emotion estimation function to propose a loading and unloading order to increase worker motivation. For example, the generation AI uses the emotion estimation function to propose a loading and unloading order to increase worker motivation. For example, it sets an order that will prevent worker fatigue. The generation AI also proposes an optimal loading and unloading order based on the worker's emotion data. For example, it sets an order that will prevent worker stress. The generation AI also dynamically adjusts the loading and unloading order to increase worker motivation based on the emotion estimation data. For example, it assigns difficult tasks when the worker is in a good mood. This makes it possible to propose a loading and unloading order that will increase worker motivation.

[0077] The loading and unloading work generation unit can automate the documents and procedures required for loading and unloading work. For example, the generation AI automates the documents and procedures required for loading and unloading work. For example, it automatically generates an unloading list and confirmation document. The generation AI also proposes the optimal automation plan based on document and procedure data. For example, it prepares the necessary documents in advance, reducing the workload of workers. The generation AI also monitors the progress of loading and unloading work in real time and automatically updates the necessary documents and procedures. For example, it automatically generates a confirmation document when the work is completed. This makes it possible to automate the documents and procedures required for loading and unloading work.

[0078] The loading and unloading work generation unit can link the loading and unloading work of luggage with inventory management and quality checks. In the loading and unloading work generation unit, for example, the generation AI generates a plan to link the loading and unloading work of luggage with inventory management and quality checks. For example, it updates inventory data simultaneously with loading and unloading. The generation AI also proposes an optimal linkage plan based on other work data. For example, it performs quality checks simultaneously with loading and unloading work. The generation AI also monitors the progress of the loading and unloading work in real time and dynamically adjusts the plan to link it with other work. For example, it links with an inventory management system to automatically update data. This allows the loading and unloading work to be linked with other work.

[0079] The loading and unloading work generation unit can use the emotion estimation function to predict the recipient's emotions and propose a loading and unloading method that will most satisfy the recipient. For example, the generation AI in the loading and unloading work generation unit uses the emotion estimation function to predict the recipient's emotions and propose a loading and unloading method that will most satisfy the recipient. For example, it sets a loading and unloading order that matches the recipient's wishes. The generation AI also proposes the optimal loading and unloading method based on the recipient's emotion data. For example, it sets a method that will least stress the recipient. The generation AI also dynamically adjusts the loading and unloading method that will most satisfy the recipient based on the emotion estimation data. For example, it loads and unloads at a time that suits the recipient's preference. In this way, it is possible to predict the recipient's emotions and propose a loading and unloading method that will most satisfy the recipient.

[0080] The operation route generation unit monitors not only the operation status of trucks but also the health status of drivers, and is able to propose optimal operation plans. For example, the generation AI in the operation route generation unit monitors the operation status of trucks and the health status of drivers, and proposes optimal operation plans. For example, it adjusts working hours according to the driver's health status. The generation AI also proposes optimal operation plans based on health data. For example, it assigns long driving hours to drivers in good health. The generation AI also monitors the driver's health status in real time, and dynamically adjusts the optimal operation plan. For example, it suggests taking a break if the driver's health status deteriorates. In this way, the operation status of trucks and the driver's health status can be monitored, and optimal operation plans can be proposed.

[0081] The operation route generation unit can generate an operation plan that minimizes fuel consumption and maintenance costs. In the operation route generation unit, for example, the generation AI generates an operation plan that minimizes fuel consumption and maintenance costs. For example, it selects a route with good fuel efficiency to reduce fuel consumption. The generation AI also proposes an optimal operation plan based on maintenance data. For example, it performs inspections when maintenance costs are low. The generation AI also monitors fuel consumption and maintenance costs in real time and dynamically adjusts the optimal operation plan. For example, if fuel prices rise, it proposes a route with good fuel efficiency. This makes it possible to generate an operation plan that minimizes fuel consumption and maintenance costs.

[0082] The operation route generation unit can use the emotion estimation function to propose a work schedule that will increase driver satisfaction. In the operation route generation unit, for example, the generation AI uses the emotion estimation function to propose a work schedule that will increase driver satisfaction. For example, it sets work hours that match the driver's preferences. The generation AI also proposes an optimal work schedule based on the driver's emotion data. For example, it sets work hours that will reduce the driver's stress. The generation AI also dynamically adjusts the work schedule to increase driver satisfaction based on the emotion estimation data. For example, it changes work hours according to the driver's preferences. This makes it possible to propose a work schedule that will increase driver satisfaction.

[0083] The operation route generation unit can analyze the management data of truck operators and make specific proposals for improving management. In the operation route generation unit, for example, the generation AI analyzes the management data of truck operators and makes specific proposals for improving management. For example, it proposes an operation plan for reducing costs. The generation AI also proposes an optimal management improvement plan based on the management data. For example, it selects a highly profitable route to maximize profits. The generation AI also monitors the management data in real time and dynamically adjusts the plan for improving management. For example, if profits decline, it proposes cost-cutting measures. This makes it possible to analyze the management data of truck operators and make specific proposals for improving management.

[0084] The operation route generation unit can promote cooperation between different truck operators and propose resource sharing and joint delivery. In the operation route generation unit, for example, the generation AI promotes cooperation between different truck operators and proposes resource sharing and joint delivery. For example, multiple operators jointly set delivery routes. The generation AI also proposes an optimal cooperation plan based on data between the operators. For example, it proposes cost-cutting measures through resource sharing. The generation AI also dynamically adjusts plans to promote cooperation between different truck operators. For example, it proposes routes that maximize the efficiency of joint delivery. This promotes cooperation between different truck operators and proposes resource sharing and joint delivery.

[0085] The operation route generation unit can use the emotion estimation function to propose a work schedule based on the emotions of the driver's family. For example, the generation AI in the operation route generation unit uses the emotion estimation function to propose a work schedule that takes into account the emotions of the driver's family. For example, it sets working hours that allow for time with family. The generation AI also proposes an optimal work schedule based on the emotional data of the driver's family. For example, it sets working hours that meet the family's wishes. The generation AI also dynamically adjusts the work schedule that takes into account the emotions of the driver's family based on the emotion estimation data. For example, it changes working hours to coincide with family events. This makes it possible to propose a work schedule that takes into account the emotions of the driver's family.

[0086] The operation route generation unit can generate plans that not only improve logistics efficiency but also minimize environmental impact. For example, the generation AI in the operation route generation unit generates plans that not only improve logistics efficiency but also minimize environmental impact. For example, it selects a route with good fuel efficiency and reduces CO2 emissions. The generation AI also proposes optimal logistics plans based on environmental data. For example, it recommends eco-driving and reduces fuel consumption. The generation AI also monitors environmental impact in real time and dynamically adjusts the optimal logistics plan. For example, it selects an environmentally friendly route and minimizes environmental impact. This makes it possible to generate plans that not only improve logistics efficiency but also minimize environmental impact.

[0087] The operation route generation unit can analyze logistics data in real time and instantly update the efficiency plan. In the operation route generation unit, for example, the generation AI analyzes logistics data in real time and instantly updates the efficiency plan. For example, it proposes the optimal route depending on traffic conditions. The generation AI also proposes the optimal efficiency plan based on the logistics data. For example, it dynamically adjusts the route depending on changes in delivery destinations. The generation AI also monitors logistics data in real time and instantly updates the efficiency plan. For example, it proposes a detour route based on traffic congestion information. This makes it possible to analyze logistics data in real time and instantly update the efficiency plan.

[0088] The operation route generation unit can use the emotion estimation function to propose an efficiency improvement plan to reduce stress for drivers and workers. For example, the generation AI in the operation route generation unit uses the emotion estimation function to propose an efficiency improvement plan to reduce stress for drivers and workers. For example, it selects a route that causes less stress. The generation AI also proposes an optimal efficiency improvement plan based on the emotion data of drivers and workers. For example, it allocates break times appropriately. The generation AI also dynamically adjusts the efficiency improvement plan to reduce stress for drivers and workers based on the emotion estimation data. For example, it suggests taking a break if stress increases. This makes it possible to propose an efficiency improvement plan to reduce stress for drivers and workers.

[0089] The operation route generation unit can generate plans that maximize customer satisfaction in addition to improving logistics efficiency. In the operation route generation unit, for example, the generation AI generates plans that maximize customer satisfaction in addition to improving logistics efficiency. For example, it adjusts delivery times to meet customer requests. The generation AI also proposes optimal logistics plans based on customer data. For example, it adjusts delivery routes to match the customer's desired times. The generation AI also monitors customer satisfaction in real time and dynamically adjusts the optimal logistics plan. For example, it changes delivery times based on customer feedback. This makes it possible to generate plans that maximize customer satisfaction in addition to improving logistics efficiency.

[0090] The operation route generation unit promotes data sharing between different logistics companies, thereby improving overall logistics efficiency. In the operation route generation unit, for example, the generation AI promotes data sharing between different logistics companies, thereby improving overall logistics efficiency. For example, it proposes the sharing of delivery routes. The generation AI also proposes an optimal logistics plan based on data between companies. For example, it proposes cost-cutting measures through joint delivery. The generation AI also dynamically adjusts plans to promote data sharing between different logistics companies. For example, it shares data in real time and proposes efficient delivery routes. This promotes data sharing between different logistics companies, thereby improving overall logistics efficiency.

[0091] The delivery route generation unit can use the emotion estimation function to predict customer emotions and propose a delivery schedule that will most satisfy the customer. In the delivery route generation unit, for example, the generation AI uses the emotion estimation function to predict customer emotions and propose a delivery schedule that will most satisfy the customer. For example, delivery will be made at the customer's desired time. The generation AI also proposes an optimal delivery schedule based on customer emotion data. For example, delivery will be made at the time that will most satisfy the customer. The generation AI also dynamically adjusts the delivery schedule that will most satisfy the customer based on the emotion estimation data. For example, delivery time will be changed according to the customer's wishes. In this way, it is possible to predict customer emotions and propose a delivery schedule that will most satisfy the customer.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The luggage placement generation unit generates an optimal luggage placement plan based on information about the luggage's size, shape, weight, and delivery destination. For example, the generation AI proposes an optimal placement based on the luggage's size and shape. The generation AI also proposes a placement that maintains truck balance, taking into account the luggage's weight. The generation AI also proposes a placement that enables efficient loading and unloading of luggage based on delivery destination information. The operation route generation unit optimizes the operation route based on information about the destination address, traffic conditions, and road congestion. For example, the generation AI proposes an optimal route based on real-time traffic information. The generation AI also analyzes past traffic patterns to propose a route that avoids traffic jams. The generation AI also proposes a route that allows delivery in the shortest time, taking into account road congestion. The loading and unloading work generation unit generates a plan that streamlines loading and unloading work based on information about luggage placement, the loading and unloading order, and work time. For example, the generation AI proposes an optimal loading and unloading order based on luggage placement information. The generation AI also proposes efficient loading and unloading work, taking into account work time. The generation AI also optimizes the loading and unloading order to reduce the burden on workers. As a result, the logistics efficiency improvement system according to the embodiment enables optimal placement of cargo, optimization of transportation routes, and efficiency improvement of loading and unloading work. For example, optimal placement of cargo improves truck loading efficiency, and optimization of transportation routes reduces delivery time. Furthermore, efficient loading and unloading reduces work time, improving overall logistics efficiency.

[0094] The luggage placement generation unit can generate a placement plan that minimizes the impact of vibration or shock based on the shape or material of the luggage. For example, the generation AI generates a placement plan that minimizes the impact of vibration or shock based on the shape and material of the luggage. For example, fragile glass products are placed in areas with less vibration, and heavy metal products are placed in the center of the truck. The generation AI also considers the placement of cushioning material to absorb vibration and shock depending on the material of the luggage. For example, electronic devices are wrapped in cushioning material to minimize the impact of vibration. The generation AI also suggests the optimal stacking method based on the shape of the luggage. For example, for box-shaped luggage, items with wider bases are placed at the bottom to maintain stability, and lighter items are stacked on top. This minimizes the impact of vibration and shock based on the shape and material of the luggage.

[0095] The luggage placement generation unit can place luggage that requires temperature control in the optimal location based on temperature sensor data. For example, the generation AI places luggage that requires temperature control in the optimal location based on temperature sensor data. For example, refrigerated items are placed in locations where cool air can easily reach them, minimizing temperature changes. The generation AI also analyzes temperature sensor data in real time and dynamically adjusts the placement of luggage that requires temperature control. For example, if the outside temperature rises, refrigerated items are moved to the center of the truck. The generation AI also proposes a plan to place insulation around luggage that requires temperature control based on temperature sensor data. For example, frozen foods are wrapped in insulation to prevent temperature changes. This allows luggage that requires temperature control to be placed in the optimal location.

[0096] The luggage placement generation unit can use the emotion estimation function to generate a luggage placement plan based on the driver's stress level. For example, the generation AI can use the emotion estimation function to analyze the driver's stress level and generate a luggage placement plan that reduces stress. For example, by removing heavy luggage from the driver's field of view. The generation AI can also monitor the driver's stress level in real time and dynamically adjust luggage placement to reduce stress. For example, if stress increases, it can suggest rearranging luggage. The generation AI can also propose a luggage placement plan to reduce driver stress based on the emotion estimation data. For example, it can arrange luggage so as not to obstruct the driver's view, improving driving comfort. This makes it possible to generate a luggage placement plan that takes the driver's stress level into account.

[0097] The cargo placement generation unit can generate plans that optimize not only cargo placement, but also air conditioning and humidity control inside the truck. For example, the generation AI generates a plan that optimizes cargo placement as well as air conditioning and humidity control inside the truck. For example, humidity-sensitive cargo is placed in a low-humidity location and the air conditioning is adjusted accordingly. The generation AI also optimizes the settings of the air conditioning system according to the cargo placement. For example, if there are many refrigerated items, the air conditioning is adjusted so that cool air is distributed evenly. The generation AI also suggests placement of cargo that requires humidity control based on data from humidity sensors. For example, paper products are placed in a low-humidity location to prevent moisture. This makes it possible to optimize the air conditioning and humidity control inside the truck.

[0098] The cargo placement generation unit can generate cargo placement plans that can be applied to other means of transportation. For example, the generation AI generates cargo placement plans that can be applied not only to trucks but also to trains and ships. For example, in the case of rail transport, cargo that is resistant to vibrations is placed at the bottom. The generation AI also takes multimodal transport into consideration and proposes the optimal cargo placement plan for each means of transportation. For example, in ship transport, heavy cargo is placed at the bottom to keep the center of gravity low. The generation AI also generates placement plans to streamline cargo transfers between different means of transportation. For example, it places cargo so that transfers from truck to train can be done smoothly. This makes it possible to generate cargo placement plans that can be applied to other means of transportation.

[0099] The package placement generation unit can use the emotion estimation function to predict the emotions of the package recipient and propose a delivery order that will most satisfy the recipient. For example, the generation AI can use the emotion estimation function to predict the emotions of the package recipient and propose a delivery order that will most satisfy the recipient. For example, it can deliver urgent packages first. The generation AI can also dynamically adjust the delivery order based on the recipient's emotion data. For example, if the recipient is not at home, it can deliver other packages first. The generation AI can also propose a delivery order that will most satisfy the recipient based on the emotion estimation data. For example, it can adjust the delivery order to match the recipient's desired time. This makes it possible to predict the emotions of the package recipient and propose a delivery order that will most satisfy the recipient.

[0100] In addition to optimizing the driving route, the driving route generation unit can also propose routes that minimize fuel consumption. For example, in addition to optimizing the driving route, the generation AI can also propose routes that minimize fuel consumption. For example, it can prioritize flat roads to reduce fuel consumption. The generation AI can also propose optimal driving routes based on fuel consumption data. For example, it can select routes with fewer traffic lights to reduce fuel waste. The generation AI can also monitor fuel consumption in real time and dynamically adjust the optimal driving route. For example, it can select routes that avoid traffic jams to minimize fuel consumption. This makes it possible to propose driving routes that minimize fuel consumption.

[0101] The route generation unit can collect traffic accident or construction information in real time and dynamically change the route based on that information. For example, the generation AI collects traffic accident and construction information in real time and dynamically changes the route based on that information. For example, if an accident occurs, it will suggest a detour route. The generation AI also proposes the optimal route based on the construction information. For example, it will choose a route that avoids roads under construction to prevent delays. The generation AI also analyzes traffic accident and construction information in real time and dynamically adjusts the optimal route. For example, if congestion occurs, it will suggest a different route. This allows the route to be dynamically changed based on traffic accident and construction information.

[0102] The route generation unit can use the emotion estimation function to generate a route that includes rest points based on the driver's fatigue level. For example, the generation AI uses the emotion estimation function to analyze the driver's fatigue level and generate a route that includes rest points. For example, if fatigue increases, it will suggest appropriate rest locations. The generation AI also monitors the driver's fatigue level in real time and dynamically adjusts the optimal route that includes rest points. For example, it will suggest taking a break after a long drive. The generation AI also suggests a route that includes rest points to reduce driver fatigue based on the emotion estimation data. For example, it will set a route that includes regular breaks. This makes it possible to generate a route that includes rest points that takes the driver's fatigue level into consideration.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The cargo placement generation unit generates an optimal cargo placement plan based on the size, shape, weight, and delivery destination of the cargo. For example, the generation AI proposes an optimal placement based on the size and shape of the cargo, and proposes a placement that maintains truck balance by taking into account the weight of the cargo. It also proposes a placement that allows for efficient loading and unloading of cargo based on delivery destination information. Step 2: The route generation unit optimizes the route based on information about the delivery destination address, traffic conditions, and road congestion. For example, the generation AI proposes the optimal route based on real-time traffic information, analyzes past traffic patterns to suggest routes that avoid congestion, and also proposes the shortest delivery route, taking road congestion into account. Step 3: The loading and unloading work generation unit generates a plan to streamline loading and unloading work based on information on the placement of luggage, the loading and unloading order, and the work time. For example, the generation AI proposes the optimal loading and unloading order based on the luggage placement information, and proposes efficient loading and unloading work taking into account the work time. It also optimizes the loading and unloading order to reduce the burden on workers.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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 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.

[0122] 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.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 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.

[0137] 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.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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 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.

[0150] 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.

[0151] 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.

[0152] 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 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a luggage placement generation unit that generates an optimal luggage placement plan based on information on the size, shape, weight, and delivery destination of the luggage; a route generation unit that optimizes the route based on information on the delivery destination address, traffic conditions, and road congestion; and a loading / unloading work generation unit that generates a plan for improving the efficiency of loading / unloading work based on information on the arrangement of luggage, the order of loading / unloading, and information on work time. A system characterized by:

2. The luggage placement generation unit Generate a placement plan that minimizes the effects of vibration or shock based on the shape or material of the cargo 2. The system of claim 1.

3. The luggage placement generation unit Based on data from temperature sensors, the luggage that requires temperature control is placed in the optimal location.

2. The system of claim 1.

4. The luggage placement generation unit Generate load planning based on driver stress levels 2. The system of claim 1.

5. The luggage placement generation unit Generate the plan that optimizes not only the placement of the loads but also the air conditioning and humidity control inside the truck.

2. The system of claim 1.

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