system

Abstract

The system according to this embodiment aims to efficiently match transportation requests with transportation companies and propose the optimal delivery route. [Solution] The system according to the embodiment comprises a matching unit, a collection unit, a proposal unit, and a control unit. The matching unit matches transportation requests with transportation companies. The collection unit collects location information of the transportation company's vehicle matched by the matching unit. The proposal unit proposes the optimal delivery route based on the location information collected by the collection unit. The control unit controls the vehicle based on the delivery route proposed by the proposal unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the matching of transportation requests and transportation providers and the proposal of an optimal delivery route are not efficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently match a transportation request and a transportation provider and propose an optimal delivery route.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a matching unit, a collection unit, a proposal unit, and a control unit. The matching unit matches transportation requests with transportation companies. The collection unit collects location information of the transportation company's vehicle matched by the matching unit. The proposal unit proposes the optimal delivery route based on the location information collected by the collection unit. The control unit controls the vehicle based on the delivery route proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently match transportation requests with transportation companies and propose the optimal delivery route. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI-LogisticsOptimizer according to an embodiment of the present invention is a logistics optimization platform that utilizes AI to solve challenges in the logistics industry. The AI-LogisticsOptimizer optimally matches companies requesting transportation with transportation companies and effectively utilizes available warehouse space. It collects and analyzes data in real time and proposes the optimal delivery route, vehicles, and warehouse space. Aiming for nationwide deployment, it is capable of supporting multiple languages ​​and currencies and complying with legal regulations. It provides an efficient and cost-effective logistics system. First, it uses an AI matching engine to match transportation requests with the most suitable transportation companies. This enables the effective use of empty and out-of-service vehicles and achieves optimal matching of warehouse space. Next, it performs demand forecasting using AI and optimizes inventory levels. This prevents excess inventory and stockouts and enables efficient inventory management. Furthermore, it uses real-time tracking and analysis functions to collect vehicle location information, transportation status, and warehouse inventory status in real time. Based on this, it proposes the optimal delivery route and warehouse space and performs dynamic route optimization. It can also propose the optimal delivery route considering traffic conditions and weather information, and propose alternative routes and warehouse space in emergencies. Furthermore, it utilizes preventative maintenance functions to monitor vehicle condition and detect signs of failure. This prevents vehicle breakdowns and improves the efficiency of transportation operations. It also monitors the condition of warehouse equipment and predicts maintenance needs to prevent equipment failures. AI-LogisticsOptimizer supports multiple languages ​​and currencies and complies with legal regulations. This enables it to handle global logistics operations and provide an efficient and cost-effective logistics system. It also works to improve the environment and workforce, using renewable energy, introducing electric vehicles, promoting the recruitment of young people and women, and providing a comfortable working environment. It also focuses on cybersecurity and privacy protection, encrypting data, strengthening access control, conducting regular security audits and vulnerability assessments, developing and training incident response plans, and complying with privacy laws and regulations. As a result, AI-LogisticsOptimizer can solve the challenges of the logistics industry and provide an efficient and cost-effective logistics system.

[0029] The AI-LogisticsOptimizer according to this embodiment comprises a matching unit, a data collection unit, a proposal unit, and a control unit. The matching unit matches transportation requests with transportation companies. The matching unit analyzes data on transportation requests and transportation companies using, for example, AI, to perform optimal matching. The data collection unit collects location information of vehicles of transportation companies matched by the matching unit. The data collection unit collects vehicle location information in real time using, for example, GPS technology. The proposal unit proposes an optimal delivery route based on the location information collected by the data collection unit. The proposal unit analyzes traffic conditions and weather information using, for example, AI, to propose an optimal delivery route. The control unit controls the vehicle based on the delivery route proposed by the proposal unit. The control unit controls the vehicle's speed and route using, for example, AI. As a result, the AI-LogisticsOptimizer according to this embodiment can consistently perform everything from matching transportation requests with transportation companies to proposing delivery routes and controlling vehicles.

[0030] The matching department matches transportation requests with transportation companies. Specifically, it uses AI to analyze data on transportation requests and transportation companies to perform optimal matching. The AI ​​analyzes information on transportation requests, such as the type of cargo, weight, delivery destination, and desired delivery time. The data on transportation companies includes vehicle type, current location, types of cargo they can transport, past performance, and ratings. Based on this data, the AI ​​selects the most suitable transportation company and matches it with the transportation request. For example, if there is a request to transport food that requires refrigeration, the AI ​​will prioritize selecting transportation companies that own vehicles with refrigeration capabilities. Also, if there is an urgent delivery request, the AI ​​will select a transportation company that is close to the destination and can respond quickly. Furthermore, the AI ​​considers past performance and ratings to select highly reliable transportation companies, thereby improving the quality of service. In this way, the matching department can achieve optimal matching between transportation requests and transportation companies, and provide efficient and reliable transportation services.

[0031] The data collection unit collects location information of vehicles from transportation companies that have been matched by the matching unit. Specifically, it collects vehicle location information in real time using GPS technology. Vehicles are equipped with GPS devices, which allow the unit to obtain information such as the vehicle's current location, speed, and direction of travel. The collected location information is transmitted to a central database and updated in real time. This allows the data collection unit to accurately know where the transportation company's vehicles are currently located and what route they are taking. Furthermore, the data collection unit can collect not only location information but also vehicle status information, such as fuel level, engine status, and tire pressure. This allows for comprehensive monitoring of vehicle operation and the provision of maintenance and support as needed. By centrally managing this information and coordinating with other systems and departments, the data collection unit can improve the efficiency and safety of transportation operations.

[0032] The Proposal Department proposes the optimal delivery route based on location information collected by the Collection Department. Specifically, it uses AI to analyze traffic conditions and weather information to propose the best delivery route. The AI ​​analyzes real-time updated traffic information, such as congestion, accident information, and road construction information, to calculate the most efficient route. It also considers weather information and proposes a safe route if bad weather such as rain or snow is expected. Furthermore, the AI ​​can predict traffic patterns for specific times of day and days of the week based on past data and propose the optimal delivery route. For example, by proposing a route that avoids weekday morning and evening rush hours, delivery times can be shortened. The Proposal Department comprehensively analyzes this information and proposes the optimal delivery route to the transportation company. As a result, the Proposal Department can achieve efficient and safe delivery and improve the quality and reliability of transportation operations.

[0033] The control unit controls the vehicle based on the delivery route proposed by the proposal unit. Specifically, it uses AI to control the vehicle's speed and route. The AI ​​controls the vehicle's navigation system according to the proposed delivery route and provides appropriate instructions to the driver. For example, it provides real-time instructions such as turning right or left at the next intersection or taking a specific road. The AI ​​also controls the vehicle's speed to support safe and efficient operation. For example, it improves fuel efficiency by avoiding sudden acceleration and deceleration and maintaining a constant speed. Furthermore, the AI ​​monitors the vehicle's status information and issues warnings to the driver if an abnormality is detected. For example, if an engine malfunction or low tire pressure is detected, it prompts the driver to take appropriate action. In this way, the control unit can comprehensively manage the vehicle's operation and improve safety and efficiency. In addition, the control unit can link with the transportation company's operation management system to monitor the operation status in real time and provide support as needed. In this way, the control unit can improve the efficiency and safety of transportation operations and increase satisfaction for both transportation companies and customers.

[0034] The matching unit can effectively utilize empty and out-of-service vehicles. For example, the matching unit uses AI to analyze data on empty and out-of-service vehicles and propose the optimal usage method. For instance, it can reallocate empty vehicles to achieve efficient transportation. It can also reduce unnecessary travel by utilizing out-of-service vehicles. In this way, by effectively utilizing empty and out-of-service vehicles, efficient logistics can be achieved.

[0035] The matching unit can perform optimal matching of warehouse space. For example, the matching unit uses AI to analyze warehouse space data and propose the optimal usage method. For instance, it can effectively utilize vacant warehouse space to achieve efficient inventory management. It can also propose the optimal layout to improve the efficiency of warehouse space utilization. In this way, by performing optimal matching of warehouse space, it becomes possible to effectively utilize vacant space.

[0036] The proposal department can suggest the optimal delivery route, taking into account traffic conditions and weather information. For example, it can use AI to analyze traffic conditions and weather information and propose the best delivery route. For instance, it can suggest the optimal route based on real-time traffic congestion information. It can also suggest the optimal route based on real-time weather information. This allows for the suggestion of more efficient delivery routes by considering traffic conditions and weather information.

[0037] The proposal department can propose alternative routes and warehouse space in emergencies. For example, the proposal department can use AI to analyze emergency situations and propose the most suitable alternative routes and warehouse space. For instance, it can propose alternative routes that allow for a rapid response in emergencies such as accidents or natural disasters. It can also propose warehouse space that can be used in emergencies. By proposing alternative routes and warehouse space that allow for a rapid response in emergencies, the stability of logistics can be improved.

[0038] The control unit can monitor the vehicle's condition and detect signs of malfunction. For example, the control unit can use AI to monitor the vehicle's condition and detect signs of malfunction. For instance, it can use sensors to detect abnormal vibrations or temperature increases in the vehicle, enabling early detection of malfunctions. It can also use data analysis to monitor the vehicle's condition and detect abnormalities. This helps prevent vehicle breakdowns and improves the efficiency of transportation operations.

[0039] The control unit can monitor the condition of warehouse equipment and predict maintenance needs. For example, the control unit can use AI to monitor the condition of warehouse equipment and predict maintenance needs. For instance, it can use sensors to detect abnormalities in warehouse equipment and identify the need for maintenance early. It can also use data analysis to monitor the condition of warehouse equipment and predict maintenance needs. This helps to prevent warehouse equipment failures and improve the efficiency of logistics operations.

[0040] The data collection unit can collect vehicle location information, transportation status, and warehouse inventory status in real time. For example, the unit uses AI to collect vehicle location information, transportation status, and warehouse inventory status in real time. For instance, it uses GPS technology to collect vehicle location information and understand transportation status in real time. It can also collect warehouse inventory status using sensors and understand inventory status in real time. This real-time information collection enables a rapid response.

[0041] The matching unit can analyze past matching history and select the optimal matching algorithm. For example, it can use AI to analyze past matching history and select the optimal matching algorithm. For instance, it can select the optimal algorithm based on past matching success rates. It can also analyze past matching failures and select algorithms that avoid those failures. Furthermore, it can select the optimal algorithm under specific conditions based on past matching history. In this way, the optimal matching algorithm can be selected by analyzing past matching history.

[0042] The matching unit can improve the accuracy of matching by considering the past performance data of the carriers during the matching process. For example, the matching unit can use AI to analyze the past performance data of carriers and improve the accuracy of matching. For example, it can improve the accuracy of matching based on the carrier's past on-time delivery rate. It can also improve the accuracy of matching based on the carrier's past customer reviews. Furthermore, it can improve the accuracy of matching based on the carrier's past accident rate. In this way, the accuracy of matching is improved by considering the carrier's past performance data.

[0043] The matching unit can perform optimal matching by considering the geographical distribution of transportation companies during the matching process. For example, the matching unit uses AI to analyze the geographical distribution of transportation companies and perform optimal matching. For instance, if a transportation company's base is nearby, it will be prioritized for matching because a quick response is possible. It can also propose the optimal route considering the geographical distribution of transportation companies. Furthermore, it can perform efficient matching by considering the geographically dispersed transportation companies. As a result, considering the geographical distribution of transportation companies enables quick and efficient matching.

[0044] The matching unit can perform matching while considering the type and capacity of the transporter's vehicles. For example, the matching unit uses AI to analyze the type and capacity of the transporter's vehicles and perform the optimal match. For example, if a large vehicle is required, it will prioritize matching with transporters that own large vehicles. It can also prioritize matching with transporters that own specialized vehicles if a specialized vehicle is required. Furthermore, it can also consider the vehicle capacity and match with the most suitable transporter. In this way, by considering the type and capacity of the transporter's vehicles, optimal matching becomes possible.

[0045] The data collection unit can analyze past collected data and select the optimal collection method. For example, it can use AI to analyze past collected data and select the optimal collection method. For instance, it can select the most efficient collection method from past collected data. It can also analyze past collected data and optimize the collection frequency. Furthermore, it can improve the collection method based on past collected data. In this way, by analyzing past collected data, the optimal collection method can be selected.

[0046] The collection unit can monitor vehicle operation status and warehouse inventory status in real time during collection and detect anomalies. For example, the collection unit can use AI to monitor vehicle operation status and warehouse inventory status in real time and detect anomalies. For instance, it can monitor vehicle operation status in real time and detect anomalies. It can also monitor warehouse inventory status in real time and detect anomalies. Furthermore, it can integrate vehicle and warehouse data to detect anomalies early. This allows for early detection of anomalies by monitoring vehicle operation status and warehouse inventory status in real time.

[0047] The data collection unit can select the optimal collection method by considering the vehicle's fuel consumption data during collection. For example, the unit can use AI to analyze the vehicle's fuel consumption data and select the optimal collection method. For instance, it can select the most efficient collection method based on fuel consumption data. It can also analyze fuel consumption data and optimize the collection frequency. Furthermore, it can improve the collection method based on fuel consumption data. This allows for the selection of an efficient collection method by considering the vehicle's fuel consumption data.

[0048] The data collection unit can improve the accuracy of data collection by referring to historical data of vehicle routes during the collection process. For example, the data collection unit can use AI to analyze historical data of vehicle routes and improve collection accuracy. For instance, it can select the optimal collection route based on historical route data. It can also analyze historical route data and optimize the collection frequency. Furthermore, it can improve the collection method based on historical route data. As a result, the accuracy of data collection is improved by referring to historical data of vehicle routes.

[0049] The proposal department can analyze past delivery route data to suggest the optimal route. For example, it can use AI to analyze past delivery route data and suggest the optimal route. It can also suggest routes that avoid congestion by analyzing past delivery route data. Furthermore, it can suggest the most efficient route based on past delivery route data. In this way, by analyzing past delivery route data, the optimal route can be suggested.

[0050] The proposal department can improve the accuracy of its proposals by considering the condition and operating status of the transporter's vehicles. For example, the proposal department can use AI to analyze the condition and operating status of the transporter's vehicles and improve the accuracy of its proposals. For instance, it can propose the optimal route based on the vehicle's condition. It can also propose the optimal route based on the operating status. Furthermore, it can integrate the vehicle's condition and operating status to propose the most efficient route. In this way, the accuracy of the proposals is improved by considering the condition and operating status of the transporter's vehicles.

[0051] The proposal department can collect traffic and weather information in real time when making a proposal and suggest the optimal route. For example, the proposal department can use AI to collect traffic and weather information in real time and suggest the optimal route. For example, it can suggest the optimal route based on real-time traffic congestion information. It can also suggest the optimal route based on real-time weather information. Furthermore, it can integrate real-time traffic and weather information to suggest the most efficient route. In this way, by collecting traffic and weather information in real time, it can suggest the optimal route.

[0052] The proposal department can suggest the optimal route when making a proposal, taking into account the type and capacity of the transporter's vehicles. For example, the proposal department can use AI to analyze the type and capacity of the transporter's vehicles and suggest the optimal route. For instance, if a large vehicle is required, it will suggest a route suitable for a large vehicle. It can also suggest a route suitable for a special vehicle if one is required. Furthermore, it can also suggest the optimal route considering the vehicle's capacity. In this way, by taking into account the type and capacity of the transporter's vehicles, the optimal route can be suggested.

[0053] The control unit can monitor vehicle operation data in real time during control, detect abnormalities, and take control accordingly. For example, the control unit can use AI to monitor vehicle operation data in real time, detect abnormalities, and take control. It can also analyze vehicle operation data to detect abnormalities early and take control. Furthermore, it can automatically take control when an abnormality occurs based on vehicle operation data. This allows for early detection of abnormalities and appropriate control by monitoring vehicle operation data in real time.

[0054] The control unit can select the optimal control method during control by considering the vehicle's fuel consumption data. For example, the control unit can use AI to analyze the vehicle's fuel consumption data and select the optimal control method. For instance, it can select the most efficient control method based on fuel consumption data. It can also analyze fuel consumption data and select a control method that optimizes fuel efficiency. Furthermore, it can improve the control method based on fuel consumption data. In this way, by considering the vehicle's fuel consumption data, an efficient control method can be selected.

[0055] The control unit can improve control accuracy by referring to historical data of the vehicle's route during control. For example, the control unit can use AI to analyze historical data of the vehicle's route and improve control accuracy. For instance, it can select the optimal control method based on the historical route data. It can also analyze historical route data to improve control accuracy. Furthermore, it can improve the control method based on historical route data. In this way, control accuracy is improved by referring to historical data of the vehicle's route.

[0056] The control unit can monitor vehicle operation status and warehouse inventory status in real time during control operations, detect abnormalities, and take appropriate action. For example, the control unit can use AI to monitor vehicle operation status and warehouse inventory status in real time, detect abnormalities, and take appropriate action. For example, it can monitor vehicle operation status in real time, detect abnormalities, and take appropriate action. It can also monitor warehouse inventory status in real time, detect abnormalities, and take appropriate action. Furthermore, it can integrate vehicle and warehouse data to detect abnormalities early and take appropriate action. As a result, by monitoring vehicle operation status and warehouse inventory status in real time, abnormalities can be detected early, enabling appropriate control.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The AI-LogisticsOptimizer can also be equipped with an energy management unit. This unit can collect vehicle fuel consumption and power consumption data in real time and propose optimal energy management methods. For example, it can suggest the most efficient route based on fuel consumption data. It can also monitor the battery level of electric vehicles and suggest the optimal placement of charging stations. Furthermore, it can monitor the use of renewable energy and promote efficient energy use. As a result, the energy management unit can optimize vehicle fuel and power consumption, reducing environmental impact and costs.

[0059] AI-LogisticsOptimizer can also be equipped with a user feedback unit. This unit can collect feedback from companies requesting transportation services and transportation companies, and use it to improve the system. For example, it can evaluate the service quality of transportation companies based on evaluations from companies requesting transportation services. It can also improve the usability and functionality of the system based on feedback from transportation companies. Furthermore, it can analyze user feedback and make suggestions to improve system performance. In this way, the user feedback unit can achieve both improved system quality and increased user satisfaction.

[0060] AI-LogisticsOptimizer can also be equipped with an environmental monitoring unit. This unit can monitor the environmental impact of transportation operations in real time and make suggestions for environmental protection. For example, it can collect vehicle exhaust gas data and propose routes to reduce emissions. It can also analyze energy consumption data in transportation operations to improve energy efficiency. Furthermore, it can monitor waste management in transportation operations and propose recycling and waste reduction measures. In this way, the environmental monitoring unit can support the reduction of the environmental impact of transportation operations and the realization of a sustainable logistics system.

[0061] AI-LogisticsOptimizer can also be equipped with a communication unit. The communication unit provides functions to facilitate communication between the requesting company and the transporter. For example, it enables real-time information sharing using a chat function. It can also provide support functions to quickly respond to problems that arise between the requesting company and the transporter. Furthermore, it can provide document management functions to facilitate confirmation of contract details and transport conditions between the requesting company and the transporter. In this way, the communication unit can streamline communication between the requesting company and the transporter, leading to the early resolution of problems and improved operational efficiency.

[0062] AI-LogisticsOptimizer can also be equipped with a predictive analytics unit. This unit can predict future fluctuations in demand and supply based on historical data and propose an optimal logistics plan. For example, it can analyze historical demand data to predict seasonal demand fluctuations. It can also analyze supply chain data to predict supply bottlenecks. Furthermore, the predictive analytics unit can propose inventory level optimizations based on demand forecasts, preventing excess inventory and stockouts. As a result, the predictive analytics unit can propose an optimal logistics plan that responds to future fluctuations in demand and supply, achieving efficient inventory management and supply chain optimization.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The matching unit matches transportation requests with transportation companies. For example, it uses AI to analyze data on transportation requests and transportation companies to perform the optimal match. Step 2: The collection unit collects location information of the transportation company's vehicles that have been matched by the matching unit. For example, it collects vehicle location information in real time using GPS technology. Step 3: The proposal unit proposes the optimal delivery route based on the location information collected by the collection unit. For example, it uses AI to analyze traffic conditions and weather information to propose the best delivery route. Step 4: The control unit controls the vehicle based on the delivery route proposed by the proposal unit. For example, it uses AI to control the vehicle's speed and route.

[0065] (Example of form 2) The AI-LogisticsOptimizer according to an embodiment of the present invention is a logistics optimization platform that utilizes AI to solve challenges in the logistics industry. The AI-LogisticsOptimizer optimally matches companies requesting transportation with transportation companies and effectively utilizes available warehouse space. It collects and analyzes data in real time and proposes the optimal delivery route, vehicles, and warehouse space. Aiming for nationwide deployment, it is capable of supporting multiple languages ​​and currencies and complying with legal regulations. It provides an efficient and cost-effective logistics system. First, it uses an AI matching engine to match transportation requests with the most suitable transportation companies. This enables the effective use of empty and out-of-service vehicles and achieves optimal matching of warehouse space. Next, it performs demand forecasting using AI and optimizes inventory levels. This prevents excess inventory and stockouts and enables efficient inventory management. Furthermore, it uses real-time tracking and analysis functions to collect vehicle location information, transportation status, and warehouse inventory status in real time. Based on this, it proposes the optimal delivery route and warehouse space and performs dynamic route optimization. It can also propose the optimal delivery route considering traffic conditions and weather information, and propose alternative routes and warehouse space in emergencies. Furthermore, it utilizes preventative maintenance functions to monitor vehicle condition and detect signs of failure. This prevents vehicle breakdowns and improves the efficiency of transportation operations. It also monitors the condition of warehouse equipment and predicts maintenance needs to prevent equipment failures. AI-LogisticsOptimizer supports multiple languages ​​and currencies and complies with legal regulations. This enables it to handle global logistics operations and provide an efficient and cost-effective logistics system. It also works to improve the environment and workforce, using renewable energy, introducing electric vehicles, promoting the recruitment of young people and women, and providing a comfortable working environment. It also focuses on cybersecurity and privacy protection, encrypting data, strengthening access control, conducting regular security audits and vulnerability assessments, developing and training incident response plans, and complying with privacy laws and regulations. As a result, AI-LogisticsOptimizer can solve the challenges of the logistics industry and provide an efficient and cost-effective logistics system.

[0066] The AI-LogisticsOptimizer according to this embodiment comprises a matching unit, a data collection unit, a proposal unit, and a control unit. The matching unit matches transportation requests with transportation companies. The matching unit analyzes data on transportation requests and transportation companies using, for example, AI, to perform optimal matching. The data collection unit collects location information of vehicles of transportation companies matched by the matching unit. The data collection unit collects vehicle location information in real time using, for example, GPS technology. The proposal unit proposes an optimal delivery route based on the location information collected by the data collection unit. The proposal unit analyzes traffic conditions and weather information using, for example, AI, to propose an optimal delivery route. The control unit controls the vehicle based on the delivery route proposed by the proposal unit. The control unit controls the vehicle's speed and route using, for example, AI. As a result, the AI-LogisticsOptimizer according to this embodiment can consistently perform everything from matching transportation requests with transportation companies to proposing delivery routes and controlling vehicles.

[0067] The matching department matches transportation requests with transportation companies. Specifically, it uses AI to analyze data on transportation requests and transportation companies to perform optimal matching. The AI ​​analyzes information on transportation requests, such as the type of cargo, weight, delivery destination, and desired delivery time. The data on transportation companies includes vehicle type, current location, types of cargo they can transport, past performance, and ratings. Based on this data, the AI ​​selects the most suitable transportation company and matches it with the transportation request. For example, if there is a request to transport food that requires refrigeration, the AI ​​will prioritize selecting transportation companies that own vehicles with refrigeration capabilities. Also, if there is an urgent delivery request, the AI ​​will select a transportation company that is close to the destination and can respond quickly. Furthermore, the AI ​​considers past performance and ratings to select highly reliable transportation companies, thereby improving the quality of service. In this way, the matching department can achieve optimal matching between transportation requests and transportation companies, and provide efficient and reliable transportation services.

[0068] The data collection unit collects location information of vehicles from transportation companies that have been matched by the matching unit. Specifically, it collects vehicle location information in real time using GPS technology. Vehicles are equipped with GPS devices, which allow the unit to obtain information such as the vehicle's current location, speed, and direction of travel. The collected location information is transmitted to a central database and updated in real time. This allows the data collection unit to accurately know where the transportation company's vehicles are currently located and what route they are taking. Furthermore, the data collection unit can collect not only location information but also vehicle status information, such as fuel level, engine status, and tire pressure. This allows for comprehensive monitoring of vehicle operation and the provision of maintenance and support as needed. By centrally managing this information and coordinating with other systems and departments, the data collection unit can improve the efficiency and safety of transportation operations.

[0069] The Proposal Department proposes the optimal delivery route based on location information collected by the Collection Department. Specifically, it uses AI to analyze traffic conditions and weather information to propose the best delivery route. The AI ​​analyzes real-time updated traffic information, such as congestion, accident information, and road construction information, to calculate the most efficient route. It also considers weather information and proposes a safe route if bad weather such as rain or snow is expected. Furthermore, the AI ​​can predict traffic patterns for specific times of day and days of the week based on past data and propose the optimal delivery route. For example, by proposing a route that avoids weekday morning and evening rush hours, delivery times can be shortened. The Proposal Department comprehensively analyzes this information and proposes the optimal delivery route to the transportation company. As a result, the Proposal Department can achieve efficient and safe delivery and improve the quality and reliability of transportation operations.

[0070] The control unit controls the vehicle based on the delivery route proposed by the proposal unit. Specifically, it uses AI to control the vehicle's speed and route. The AI ​​controls the vehicle's navigation system according to the proposed delivery route and provides appropriate instructions to the driver. For example, it provides real-time instructions such as turning right or left at the next intersection or taking a specific road. The AI ​​also controls the vehicle's speed to support safe and efficient operation. For example, it improves fuel efficiency by avoiding sudden acceleration and deceleration and maintaining a constant speed. Furthermore, the AI ​​monitors the vehicle's status information and issues warnings to the driver if an abnormality is detected. For example, if an engine malfunction or low tire pressure is detected, it prompts the driver to take appropriate action. In this way, the control unit can comprehensively manage the vehicle's operation and improve safety and efficiency. In addition, the control unit can link with the transportation company's operation management system to monitor the operation status in real time and provide support as needed. In this way, the control unit can improve the efficiency and safety of transportation operations and increase satisfaction for both transportation companies and customers.

[0071] The matching unit can effectively utilize empty and out-of-service vehicles. For example, the matching unit uses AI to analyze data on empty and out-of-service vehicles and propose the optimal usage method. For instance, it can reallocate empty vehicles to achieve efficient transportation. It can also reduce unnecessary travel by utilizing out-of-service vehicles. In this way, by effectively utilizing empty and out-of-service vehicles, efficient logistics can be achieved.

[0072] The matching unit can perform optimal matching of warehouse space. For example, the matching unit uses AI to analyze warehouse space data and propose the optimal usage method. For instance, it can effectively utilize vacant warehouse space to achieve efficient inventory management. It can also propose the optimal layout to improve the efficiency of warehouse space utilization. In this way, by performing optimal matching of warehouse space, it becomes possible to effectively utilize vacant space.

[0073] The proposal department can suggest the optimal delivery route, taking into account traffic conditions and weather information. For example, it can use AI to analyze traffic conditions and weather information and propose the best delivery route. For instance, it can suggest the optimal route based on real-time traffic congestion information. It can also suggest the optimal route based on real-time weather information. This allows for the suggestion of more efficient delivery routes by considering traffic conditions and weather information.

[0074] The proposal department can propose alternative routes and warehouse space in emergencies. For example, the proposal department can use AI to analyze emergency situations and propose the most suitable alternative routes and warehouse space. For instance, it can propose alternative routes that allow for a rapid response in emergencies such as accidents or natural disasters. It can also propose warehouse space that can be used in emergencies. By proposing alternative routes and warehouse space that allow for a rapid response in emergencies, the stability of logistics can be improved.

[0075] The control unit can monitor the vehicle's condition and detect signs of malfunction. For example, the control unit can use AI to monitor the vehicle's condition and detect signs of malfunction. For instance, it can use sensors to detect abnormal vibrations or temperature increases in the vehicle, enabling early detection of malfunctions. It can also use data analysis to monitor the vehicle's condition and detect abnormalities. This helps prevent vehicle breakdowns and improves the efficiency of transportation operations.

[0076] The control unit can monitor the condition of warehouse equipment and predict maintenance needs. For example, the control unit can use AI to monitor the condition of warehouse equipment and predict maintenance needs. For instance, it can use sensors to detect abnormalities in warehouse equipment and identify the need for maintenance early. It can also use data analysis to monitor the condition of warehouse equipment and predict maintenance needs. This helps to prevent warehouse equipment failures and improve the efficiency of logistics operations.

[0077] The data collection unit can collect vehicle location information, transportation status, and warehouse inventory status in real time. For example, the unit uses AI to collect vehicle location information, transportation status, and warehouse inventory status in real time. For instance, it uses GPS technology to collect vehicle location information and understand transportation status in real time. It can also collect warehouse inventory status using sensors and understand inventory status in real time. This real-time information collection enables a rapid response.

[0078] The matching unit can estimate the user's emotions and adjust the matching priority based on those emotions. For example, the matching unit uses an emotion engine or generative AI to estimate the user's emotions and adjusts the matching priority based on those emotions. For instance, if the user is in a hurry, it will prioritize matching them with a carrier that can respond quickly. If the user is relaxed, it can also prioritize matching them with a cost-effective carrier. Furthermore, if the user is feeling anxious, it can also prioritize matching them with a reliable carrier. By adjusting the matching priority according to the user's emotions, more appropriate matching becomes possible.

[0079] The matching unit can analyze past matching history and select the optimal matching algorithm. For example, it can use AI to analyze past matching history and select the optimal matching algorithm. For instance, it can select the optimal algorithm based on past matching success rates. It can also analyze past matching failures and select algorithms that avoid those failures. Furthermore, it can select the optimal algorithm under specific conditions based on past matching history. In this way, the optimal matching algorithm can be selected by analyzing past matching history.

[0080] The matching unit can improve the accuracy of matching by considering the past performance data of the carriers during the matching process. For example, the matching unit can use AI to analyze the past performance data of carriers and improve the accuracy of matching. For example, it can improve the accuracy of matching based on the carrier's past on-time delivery rate. It can also improve the accuracy of matching based on the carrier's past customer reviews. Furthermore, it can improve the accuracy of matching based on the carrier's past accident rate. In this way, the accuracy of matching is improved by considering the carrier's past performance data.

[0081] The matching unit can estimate the user's emotions and adjust the display method of the matching results based on the estimated emotions. For example, the matching unit estimates the user's emotions using an emotion engine or generative AI and adjusts the display method of the matching results based on the estimated emotions. For example, if the user is nervous, it can provide a simple and highly visible display method. If the user is relaxed, it can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. In this way, by adjusting the display method according to the user's emotions, it becomes possible to provide more appropriate information.

[0082] The matching unit can perform optimal matching by considering the geographical distribution of transportation companies during the matching process. For example, the matching unit uses AI to analyze the geographical distribution of transportation companies and perform optimal matching. For instance, if a transportation company's base is nearby, it will be prioritized for matching because a quick response is possible. It can also propose the optimal route considering the geographical distribution of transportation companies. Furthermore, it can perform efficient matching by considering the geographically dispersed transportation companies. As a result, considering the geographical distribution of transportation companies enables quick and efficient matching.

[0083] The matching unit can perform matching while considering the type and capacity of the transporter's vehicles. For example, the matching unit uses AI to analyze the type and capacity of the transporter's vehicles and perform the optimal match. For example, if a large vehicle is required, it will prioritize matching with transporters that own large vehicles. It can also prioritize matching with transporters that own specialized vehicles if a specialized vehicle is required. Furthermore, it can also consider the vehicle capacity and match with the most suitable transporter. In this way, by considering the type and capacity of the transporter's vehicles, optimal matching becomes possible.

[0084] The data collection unit can estimate the user's emotions and adjust the frequency of location data collection based on those emotions. For example, the data collection unit can use an emotion engine or generative AI to estimate the user's emotions and adjust the frequency of location data collection based on those emotions. For example, if the user is in a hurry, the frequency of location data collection can be increased. Conversely, if the user is relaxed, the frequency of location data collection can be decreased. Furthermore, if the user is feeling anxious, the frequency of location data collection can be appropriately adjusted. By adjusting the frequency of location data collection according to the user's emotions, more appropriate information can be collected.

[0085] The data collection unit can analyze past collected data and select the optimal collection method. For example, it can use AI to analyze past collected data and select the optimal collection method. For instance, it can select the most efficient collection method from past collected data. It can also analyze past collected data and optimize the collection frequency. Furthermore, it can improve the collection method based on past collected data. In this way, by analyzing past collected data, the optimal collection method can be selected.

[0086] The collection unit can monitor vehicle operation status and warehouse inventory status in real time during collection and detect anomalies. For example, the collection unit can use AI to monitor vehicle operation status and warehouse inventory status in real time and detect anomalies. For instance, it can monitor vehicle operation status in real time and detect anomalies. It can also monitor warehouse inventory status in real time and detect anomalies. Furthermore, it can integrate vehicle and warehouse data to detect anomalies early. This allows for early detection of anomalies by monitoring vehicle operation status and warehouse inventory status in real time.

[0087] The data collection unit can estimate the user's emotions and adjust the display method of the collected data based on the estimated emotions. For example, the data collection unit can estimate the user's emotions using an emotion engine or generative AI and adjust the display method of the collected data based on the estimated emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. In this way, by adjusting the display method according to the user's emotions, it becomes possible to provide more appropriate information.

[0088] The data collection unit can select the optimal collection method by considering the vehicle's fuel consumption data during collection. For example, the unit can use AI to analyze the vehicle's fuel consumption data and select the optimal collection method. For instance, it can select the most efficient collection method based on fuel consumption data. It can also analyze fuel consumption data and optimize the collection frequency. Furthermore, it can improve the collection method based on fuel consumption data. This allows for the selection of an efficient collection method by considering the vehicle's fuel consumption data.

[0089] The data collection unit can improve the accuracy of data collection by referring to historical data of vehicle routes during the collection process. For example, the data collection unit can use AI to analyze historical data of vehicle routes and improve collection accuracy. For instance, it can select the optimal collection route based on historical route data. It can also analyze historical route data and optimize the collection frequency. Furthermore, it can improve the collection method based on historical route data. As a result, the accuracy of data collection is improved by referring to historical data of vehicle routes.

[0090] The suggestion function can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, it can use an emotion engine or generative AI to estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For instance, if the user is nervous, it can provide a simple and highly visible suggestion. If the user is relaxed, it can provide a suggestion that includes more detailed information. Furthermore, if the user is in a hurry, it can provide a suggestion that gets straight to the point. By adjusting the way suggestions are presented according to the user's emotions, it becomes possible to provide more appropriate information.

[0091] The proposal department can analyze past delivery route data to suggest the optimal route. For example, it can use AI to analyze past delivery route data and suggest the optimal route. It can also suggest routes that avoid congestion by analyzing past delivery route data. Furthermore, it can suggest the most efficient route based on past delivery route data. In this way, by analyzing past delivery route data, the optimal route can be suggested.

[0092] The proposal department can improve the accuracy of its proposals by considering the condition and operating status of the transporter's vehicles. For example, the proposal department can use AI to analyze the condition and operating status of the transporter's vehicles and improve the accuracy of its proposals. For instance, it can propose the optimal route based on the vehicle's condition. It can also propose the optimal route based on the operating status. Furthermore, it can integrate the vehicle's condition and operating status to propose the most efficient route. In this way, the accuracy of the proposals is improved by considering the condition and operating status of the transporter's vehicles.

[0093] The suggestion function can estimate the user's emotions and prioritize suggestions based on those emotions. For example, it can use an emotion engine or generative AI to estimate the user's emotions and prioritize suggestions based on those emotions. For instance, if the user is in a hurry, it will prioritize suggestions that allow for a quick response. If the user is relaxed, it can also prioritize cost-effective suggestions. Furthermore, if the user is feeling anxious, it can prioritize highly reliable suggestions. By prioritizing suggestions according to the user's emotions, it becomes possible to provide more appropriate suggestions.

[0094] The proposal department can collect traffic and weather information in real time when making a proposal and suggest the optimal route. For example, the proposal department can use AI to collect traffic and weather information in real time and suggest the optimal route. For example, it can suggest the optimal route based on real-time traffic congestion information. It can also suggest the optimal route based on real-time weather information. Furthermore, it can integrate real-time traffic and weather information to suggest the most efficient route. In this way, by collecting traffic and weather information in real time, it can suggest the optimal route.

[0095] The proposal department can suggest the optimal route when making a proposal, taking into account the type and capacity of the transporter's vehicles. For example, the proposal department can use AI to analyze the type and capacity of the transporter's vehicles and suggest the optimal route. For instance, if a large vehicle is required, it will suggest a route suitable for a large vehicle. It can also suggest a route suitable for a special vehicle if one is required. Furthermore, it can also suggest the optimal route considering the vehicle's capacity. In this way, by taking into account the type and capacity of the transporter's vehicles, the optimal route can be suggested.

[0096] The control unit can estimate the user's emotions and adjust the vehicle's control method based on those emotions. For example, the control unit can estimate the user's emotions using an emotion engine or generative AI and adjust the vehicle's control method based on those emotions. For example, if the user is in a hurry, a control method prioritizing speed may be adopted. If the user is relaxed, a control method prioritizing fuel efficiency may be adopted. Furthermore, if the user is feeling anxious, a control method prioritizing safety may be adopted. By adjusting the vehicle's control method according to the user's emotions, more appropriate operation becomes possible.

[0097] The control unit can monitor vehicle operation data in real time during control, detect abnormalities, and take control accordingly. For example, the control unit can use AI to monitor vehicle operation data in real time, detect abnormalities, and take control. It can also analyze vehicle operation data to detect abnormalities early and take control. Furthermore, it can automatically take control when an abnormality occurs based on vehicle operation data. This allows for early detection of abnormalities and appropriate control by monitoring vehicle operation data in real time.

[0098] The control unit can select the optimal control method during control by considering the vehicle's fuel consumption data. For example, the control unit can use AI to analyze the vehicle's fuel consumption data and select the optimal control method. For instance, it can select the most efficient control method based on fuel consumption data. It can also analyze fuel consumption data and select a control method that optimizes fuel efficiency. Furthermore, it can improve the control method based on fuel consumption data. In this way, by considering the vehicle's fuel consumption data, an efficient control method can be selected.

[0099] The control unit can estimate the user's emotions and adjust the display method of the control results based on the estimated emotions. For example, the control unit can estimate the user's emotions using an emotion engine or generative AI and adjust the display method of the control results based on the estimated emotions. For example, if the user is nervous, it can provide a simple and highly visible display method. If the user is relaxed, it can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, it becomes possible to provide more appropriate information.

[0100] The control unit can improve control accuracy by referring to historical data of the vehicle's route during control. For example, the control unit can use AI to analyze historical data of the vehicle's route and improve control accuracy. For instance, it can select the optimal control method based on the historical route data. It can also analyze historical route data to improve control accuracy. Furthermore, it can improve the control method based on historical route data. In this way, control accuracy is improved by referring to historical data of the vehicle's route.

[0101] The control unit can monitor vehicle operation status and warehouse inventory status in real time during control operations, detect abnormalities, and take appropriate action. For example, the control unit can use AI to monitor vehicle operation status and warehouse inventory status in real time, detect abnormalities, and take appropriate action. For example, it can monitor vehicle operation status in real time, detect abnormalities, and take appropriate action. It can also monitor warehouse inventory status in real time, detect abnormalities, and take appropriate action. Furthermore, it can integrate vehicle and warehouse data to detect abnormalities early and take appropriate action. As a result, by monitoring vehicle operation status and warehouse inventory status in real time, abnormalities can be detected early, enabling appropriate control.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The AI-LogisticsOptimizer can also be equipped with an energy management unit. This unit can collect vehicle fuel consumption and power consumption data in real time and propose optimal energy management methods. For example, it can suggest the most efficient route based on fuel consumption data. It can also monitor the battery level of electric vehicles and suggest the optimal placement of charging stations. Furthermore, it can monitor the use of renewable energy and promote efficient energy use. As a result, the energy management unit can optimize vehicle fuel and power consumption, reducing environmental impact and costs.

[0104] AI-LogisticsOptimizer can also be equipped with a user feedback unit. This unit can collect feedback from companies requesting transportation services and transportation companies, and use it to improve the system. For example, it can evaluate the service quality of transportation companies based on evaluations from companies requesting transportation services. It can also improve the usability and functionality of the system based on feedback from transportation companies. Furthermore, it can analyze user feedback and make suggestions to improve system performance. In this way, the user feedback unit can achieve both improved system quality and increased user satisfaction.

[0105] AI-LogisticsOptimizer can also be equipped with an environmental monitoring unit. This unit can monitor the environmental impact of transportation operations in real time and make suggestions for environmental protection. For example, it can collect vehicle exhaust gas data and propose routes to reduce emissions. It can also analyze energy consumption data in transportation operations to improve energy efficiency. Furthermore, it can monitor waste management in transportation operations and propose recycling and waste reduction measures. In this way, the environmental monitoring unit can support the reduction of the environmental impact of transportation operations and the realization of a sustainable logistics system.

[0106] AI-LogisticsOptimizer can also be equipped with a communication unit. The communication unit provides functions to facilitate communication between the requesting company and the transporter. For example, it enables real-time information sharing using a chat function. It can also provide support functions to quickly respond to problems that arise between the requesting company and the transporter. Furthermore, it can provide document management functions to facilitate confirmation of contract details and transport conditions between the requesting company and the transporter. In this way, the communication unit can streamline communication between the requesting company and the transporter, leading to the early resolution of problems and improved operational efficiency.

[0107] AI-LogisticsOptimizer can also be equipped with a predictive analytics unit. This unit can predict future fluctuations in demand and supply based on historical data and propose an optimal logistics plan. For example, it can analyze historical demand data to predict seasonal demand fluctuations. It can also analyze supply chain data to predict supply bottlenecks. Furthermore, the predictive analytics unit can propose inventory level optimizations based on demand forecasts, preventing excess inventory and stockouts. As a result, the predictive analytics unit can propose an optimal logistics plan that responds to future fluctuations in demand and supply, achieving efficient inventory management and supply chain optimization.

[0108] The AI-LogisticsOptimizer can also be equipped with an emotion estimation unit. This unit can estimate the user's emotions and adjust the system's behavior based on the estimated emotions. For example, if the user is stressed, the system interface can be simplified to reduce the burden of operation. If the user is relaxed, it can provide detailed information and perform deeper analysis. Furthermore, if the user is in a hurry, it can prioritize functions that allow for quick responses. In this way, the emotion estimation unit can adjust the system's behavior according to the user's emotions, providing a more comfortable user experience.

[0109] AI-LogisticsOptimizer can also be equipped with an emotional feedback unit. This unit provides real-time feedback on user emotions, which can be used to improve the system. For example, if a user is dissatisfied, it can identify the cause and suggest improvements to the system. If a user is satisfied, it can analyze the factors contributing to that satisfaction and suggest improvements to provide similar satisfaction to other users. Furthermore, the emotional feedback unit can accumulate user emotional data and analyze long-term trends to continuously improve the system. This allows the emotional feedback unit to improve the system based on user emotions, thereby increasing user satisfaction.

[0110] AI-LogisticsOptimizer can also be equipped with an emotion analysis unit. This unit can analyze user emotions in detail and propose solutions to optimize system performance. For example, if a user is stressed, it can identify the cause and improve the system's operation and interface. If a user is satisfied, it can analyze the factors contributing to that satisfaction and propose improvements to provide similar satisfaction to other users. Furthermore, the emotion analysis unit can accumulate user emotion data and analyze long-term trends to continuously improve the system. This allows the emotion analysis unit to optimize system performance based on user emotions, thereby improving user satisfaction.

[0111] AI-LogisticsOptimizer can also be equipped with an emotion prediction unit. This unit can predict future emotions based on the user's past behavior and feedback, and adjust the system's operation accordingly. For example, it can analyze situations where the user previously experienced stress and take preventative measures if similar situations occur. It can also analyze situations where the user previously felt satisfied and suggest ways to provide similar satisfaction. Furthermore, the emotion prediction unit can accumulate user emotion data and analyze long-term trends to continuously improve the system. This allows the emotion prediction unit to adjust the system's operation based on the user's emotions, providing a more comfortable user experience.

[0112] AI-LogisticsOptimizer can also be equipped with an emotion-adaptive unit. This unit can adapt the system's behavior in real time according to the user's emotions. For example, if the user is stressed, it can simplify the system interface to reduce the burden of operation. Conversely, if the user is relaxed, it can provide detailed information and perform deeper analysis. Furthermore, if the user is in a hurry, it can prioritize functions that allow for quick responses. In this way, the emotion-adaptive unit can adapt the system's behavior in real time according to the user's emotions, providing a more comfortable user experience.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The matching unit matches transportation requests with transportation companies. For example, it uses AI to analyze data on transportation requests and transportation companies to perform the optimal match. Step 2: The collection unit collects location information of the transportation company's vehicles that have been matched by the matching unit. For example, it collects vehicle location information in real time using GPS technology. Step 3: The proposal unit proposes the optimal delivery route based on the location information collected by the collection unit. For example, it uses AI to analyze traffic conditions and weather information to propose the best delivery route. Step 4: The control unit controls the vehicle based on the delivery route proposed by the proposal unit. For example, it uses AI to control the vehicle's speed and route.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0118] Each of the multiple elements described above, including the matching unit, collection unit, proposal unit, and control unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the matching unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The collection unit is implemented by the GPS function of the smart device 14 or the specific processing unit 290 of the data processing device 12. The proposal unit is implemented by the specific processing unit 290 of the data processing device 12, for example, and analyzes traffic conditions and weather information. The control unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the matching unit, collection unit, suggestion unit, and control unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the matching unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The collection unit is implemented by the GPS function of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, and analyzes traffic conditions and weather information. The control unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the matching unit, collection unit, suggestion unit, and control unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the matching unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The collection unit is implemented by the GPS function of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, and analyzes traffic conditions and weather information. The control unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

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

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the matching unit, collection unit, proposal unit, and control unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the matching unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The collection unit is implemented by the GPS function of the robot 414 or the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, and analyzes traffic conditions and weather information. The control unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0168] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0170] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0171] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0172] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0177] 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.

[0178] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0179] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0181] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0185] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0186] (Note 1) A matching department that connects transportation requests with transportation companies, A collection unit that collects location information of vehicles of carriers matched by the matching unit, A proposal unit that proposes the optimal delivery route based on the location information collected by the collection unit, The system includes a control unit that controls the vehicle based on the delivery route proposed by the proposal unit. A system characterized by the following features. (Note 2) The matching unit is Effective use of empty and out-of-service vehicles. The system described in Appendix 1, characterized by the features described herein. (Note 3) The matching unit is Optimal matching of warehouse space The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose the optimal delivery route, taking into account traffic conditions and weather information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We propose alternative routes and warehouse space in case of emergencies. The system described in Appendix 1, characterized by the features described herein. (Note 6) The control unit, It monitors the vehicle's condition and detects signs of malfunction. The system described in Appendix 1, characterized by the features described herein. (Note 7) The control unit, Monitoring the condition of warehouse equipment and predicting maintenance needs. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Collects vehicle location information, transportation status, and warehouse inventory status in real time. The system described in Appendix 1, characterized by the features described herein. (Note 9) The matching unit is It estimates the user's emotions and adjusts the matching priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The matching unit is Analyze past matching history and select the optimal matching algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 11) The matching unit is During the matching process, we improve the accuracy of the matching by considering the past performance data of the shipping companies. The system described in Appendix 1, characterized by the features described herein. (Note 12) The matching unit is The system estimates the user's emotions and adjusts how matching results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The matching unit is During the matching process, the geographical distribution of transportation companies is taken into consideration to ensure the best possible match. The system described in Appendix 1, characterized by the features described herein. (Note 14) The matching unit is During the matching process, the type and capacity of the transporter's vehicles are taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of location data collection based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is Analyze past collected data and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is During collection, the system monitors vehicle operation status and warehouse inventory status in real time to detect any abnormalities. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned collection unit is It estimates the user's emotions and adjusts how collected data is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned collection unit is During data collection, the optimal collection method is selected considering the vehicle's fuel consumption data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned collection unit is During data collection, we improve the accuracy of the collection by referring to historical data on the vehicle's operating routes. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, we analyze past delivery route data to suggest the optimal route. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, we improve its accuracy by taking into account the condition and operating status of the transporter's vehicles. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, the system collects real-time traffic and weather information to suggest the optimal route. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, we will consider the type and capacity of the transporter's vehicles to suggest the optimal route. The system described in Appendix 1, characterized by the features described herein. (Note 27) The control unit, The system estimates the user's emotions and adjusts the vehicle's control methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The control unit, During control, vehicle operation data is monitored in real time, and any abnormalities are detected and controlled accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 29) The control unit, During control, the optimal control method is selected by considering the vehicle's fuel consumption data. The system described in Appendix 1, characterized by the features described herein. (Note 30) The control unit, It estimates the user's emotions and adjusts how the control results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The control unit, During control, the system improves control accuracy by referencing historical data of the vehicle's operating route. The system described in Appendix 1, characterized by the features described herein. (Note 32) The control unit, During control, the system monitors vehicle operation status and warehouse inventory status in real time, detects abnormalities, and implements control measures. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A matching department that connects transportation requests with transportation companies, A collection unit that collects location information of vehicles of carriers matched by the matching unit, A proposal unit that proposes the optimal delivery route based on the location information collected by the collection unit, The system includes a control unit that controls the vehicle based on the delivery route proposed by the proposal unit. A system characterized by the following features.

2. The matching unit is Effective use of empty and out-of-service vehicles. The system according to feature 1.

3. The matching unit is Optimal matching of warehouse space The system according to feature 1.

4. The aforementioned proposal section is, We propose the optimal delivery route, taking into account traffic conditions and weather information. The system according to feature 1.

5. The aforementioned proposal section is, We propose alternative routes and warehouse space in case of emergencies. The system according to feature 1.

6. The control unit, It monitors the vehicle's condition and detects signs of malfunction. The system according to feature 1.

7. The control unit, Monitoring the condition of warehouse equipment and predicting maintenance needs. The system according to feature 1.

8. The aforementioned collection unit is Collects vehicle location information, transportation status, and warehouse inventory status in real time. The system according to feature 1.

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