System

Abstract

An object of a system according to an embodiment is to realize an efficient operation of a logistics infrastructure.SOLUTION: A system according to an embodiment includes a demand prediction unit, an infrastructure sharing unit, and a delivery route optimization unit. The demand prediction unit performs demand prediction using the generated AI. The infrastructure sharing unit uses the cloud technology to share the logistics infrastructure. The delivery route optimizing unit generates an optimal delivery route using the generated AI.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have faced challenges such as high maintenance and investment costs for logistics infrastructure and difficulty in efficient operation.

[0005] The system according to the embodiment aims to realize efficient operation of logistics infrastructure. [Means for solving the problem]

[0006] The system according to the embodiment includes a demand forecasting unit, an infrastructure sharing unit, and a delivery route optimization unit. The demand forecasting unit performs demand forecasting using a generation AI. The infrastructure sharing unit shares logistics infrastructure using cloud technology. The delivery route optimization unit generates an optimal delivery route using the generation AI. [Effects of the Invention]

[0007] The system according to the embodiment can realize efficient operation of the logistics infrastructure. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The logistics efficiency system according to an embodiment of the present invention utilizes AI prediction and cloud technology to improve the efficiency of logistics. This system reduces the base maintenance and investment costs incurred when each delivery company independently secures its own logistics network, and suppresses increases in transportation costs by allowing the shared use of logistics infrastructure. As a result, the logistics efficiency system can improve logistics efficiency and reduce costs.

[0029] A logistics efficiency improvement system according to an embodiment includes a demand forecasting unit, an infrastructure sharing unit, and a delivery route optimization unit. The demand forecasting unit performs demand forecasting using a generation AI. For example, the generation AI analyzes data such as past logistics data, market trends, and seasonal fluctuations to predict future demand. The generation AI can also predict the amount of logistics demand that will occur in a particular region at a specific time. The generation AI performs demand forecasting using, for example, a deep learning model or natural language generation technology. The infrastructure sharing unit uses cloud technology to share logistics infrastructure. For example, logistics data is aggregated on the cloud so that each company can access it in real time. The data shared on the cloud includes delivery routes, delivery schedules, inventory information, and the like. Cloud technology is realized using, for example, a cloud service provider and a type of cloud infrastructure. The delivery route optimization unit generates an optimal delivery route using the generation AI. For example, the generation AI analyzes demand forecast data, real-time traffic information, location information of delivery destinations, and the like to propose an optimal delivery route. The generation AI generates an optimal delivery route based on, for example, the demand forecast data, real-time traffic information, and location information of delivery destinations. As a result, the logistics efficiency system can achieve logistics efficiency and cost reduction.

[0030] The demand forecasting unit can identify factors that cause demand fluctuations and dynamically adjust the forecasting model based on those factors. The demand forecasting unit, for example, uses a generation AI to identify factors that cause demand fluctuations in addition to the demand forecast. For example, it analyzes data such as weather, economic indicators, and consumer behavior to identify factors that cause demand fluctuations. This allows the demand forecasting model to be dynamically adjusted, resulting in more accurate forecasts. In addition, when the generation AI performs demand forecasting, it identifies factors that cause demand fluctuations and dynamically adjusts the forecasting model based on those factors. For example, it analyzes the impact of specific events and promotions on demand and reflects this in the forecasting model. In addition to the demand forecasting, the generation AI also identifies factors that cause demand fluctuations and dynamically adjusts the forecasting model based on those factors. For example, it takes seasonal fluctuations and market trends into account and updates the demand forecasting model in real time. This improves the accuracy of demand forecasting.

[0031] The demand forecasting unit can analyze external data such as social media or news articles to predict sudden fluctuations in demand. For example, when the generation AI performs demand forecasting, the demand forecasting unit analyzes external data such as social media and news articles to predict sudden fluctuations in demand. For example, if there is a sudden increase in buzz about a particular product, it predicts the possibility of a sudden increase in demand for that product. External data such as social media and news articles is also input into the generation AI to predict sudden fluctuations in demand. For example, the impact of natural disasters or political events on demand is analyzed and reflected in the prediction model. Furthermore, when the generation AI performs demand forecasting, it analyzes external data such as social media and news articles to predict sudden fluctuations in demand. For example, the impact of specific events or campaigns on demand is analyzed in real time and the prediction model is updated. This makes it possible to respond to sudden fluctuations in demand.

[0032] The infrastructure sharing unit visualizes the real-time usage status of each company's logistics resources, enabling efficient resource allocation. For example, in addition to sharing data on the cloud, the infrastructure sharing unit builds a system that visualizes the real-time usage status of each company's logistics resources (for example, warehouses and vehicles). For example, it displays the availability of each company's warehouses and the operating status of their vehicles in real time. It also visualizes the real-time usage status of each company's logistics resources on the cloud and allocates resources efficiently. For example, it allocates resources preferentially to areas where demand is high. In addition to sharing data on the cloud, it visualizes the real-time usage status of each company's logistics resources and allocates resources efficiently. For example, it can grasp resource surpluses and shortages in real time and allocate resources appropriately. This enables efficient allocation of logistics resources.

[0033] The infrastructure sharing unit can integrate the logistics data of each company and provide a dashboard for optimizing overall logistics efficiency. The infrastructure sharing unit, for example, uses cloud technology to integrate the logistics data of each company and provide a dashboard for optimizing overall logistics efficiency. For example, it centrally manages each company's delivery routes and inventory status. It also integrates each company's logistics data on the cloud and provides a dashboard for optimizing overall logistics efficiency. For example, it proposes optimal delivery routes based on real-time data. It also uses cloud technology to integrate the logistics data of each company and provide a dashboard for optimizing overall logistics efficiency. For example, it visualizes each company's resource usage status and allocates resources efficiently. This makes it possible to optimize overall logistics efficiency.

[0034] The delivery route optimization unit can dynamically recalculate the route in response to real-time changes in the situation. For example, using a generation AI, the delivery route optimization unit dynamically recalculates the route in response to real-time changes in the situation during delivery (for example, traffic congestion or weather) in addition to optimizing the delivery route. For example, if a traffic congestion occurs, it proposes an optimal detour route. In addition, real-time changes in the situation during delivery are input into the generation AI, which dynamically recalculates the route. For example, it proposes a safe route in response to changes in the weather. In addition to optimizing the delivery route, the generation AI dynamically recalculates the route in response to real-time changes in the situation during delivery. For example, it proposes an optimal route based on accident or construction information. This makes it possible to optimize delivery routes in response to real-time changes in the situation.

[0035] The delivery route optimization unit can optimize routes taking into account energy consumption or environmental load. For example, the delivery route optimization unit adds optimization that takes into account energy consumption and environmental load to the delivery route proposed by the generation AI. For example, it may prioritize routes that consume less fuel. It also inputs energy consumption and environmental load into the generation AI to propose the optimal delivery route. For example, it may select a route with low CO2 emissions. It also adds optimization that takes into account energy consumption and environmental load to the delivery route proposed by the generation AI. For example, it may propose a route that encourages eco-driving. This makes it possible to optimize delivery routes taking into account energy consumption and environmental load.

[0036] The delivery route optimization unit can simultaneously optimize the schedule in addition to optimizing the route. For example, the delivery route optimization unit uses generation AI to simultaneously optimize not only the delivery route but also the delivery schedule. For example, it proposes the optimal delivery time based on demand forecast data. In addition, the optimized delivery route and schedule are input into the generation AI to propose an efficient delivery plan. For example, it sets a schedule that matches the business hours of the delivery destination. In addition, the generation AI is used to simultaneously optimize not only the delivery route but also the delivery schedule. For example, it proposes the optimal delivery time to avoid traffic congestion. This allows the delivery route and schedule to be optimized simultaneously.

[0037] The delivery route optimization unit generates a maintenance schedule based on the optimized route, thereby improving vehicle utilization rates. The delivery route optimization unit, for example, uses a generation AI to generate a vehicle maintenance schedule based on the optimized delivery route. For example, it plans maintenance after long-distance driving. It also generates a vehicle maintenance schedule based on the optimized delivery route data. For example, it grasps the vehicle's operating status in real time and proposes appropriate maintenance timing. It also uses a generation AI to generate a vehicle maintenance schedule based on the optimized delivery route, thereby improving vehicle utilization rates. For example, it optimizes the timing of maintenance and minimizes vehicle downtime. This makes it possible to generate a maintenance schedule that improves vehicle utilization rates.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The logistics efficiency system can further be equipped with an energy management unit. The energy management unit monitors the fuel and power consumption of each delivery vehicle in real time and promotes efficient energy use. For example, it can provide eco-driving guidance to vehicles with high fuel consumption. It can also suggest the introduction of energy-efficient equipment to warehouses with high power consumption. Furthermore, the energy management unit can share each company's energy usage data on the cloud and provide a dashboard for optimizing overall energy efficiency. This allows the logistics efficiency system to reduce energy consumption and the environmental impact.

[0040] The logistics efficiency system can also be equipped with a safety management department. The safety management department monitors the operating status of each delivery vehicle in real time and promotes safe driving. For example, it analyzes the frequency of sudden braking and sudden acceleration and provides guidance on safe driving. It can also share driver driving status on the cloud and visualize the overall safe driving status. Furthermore, the safety management department can identify routes and time periods with a high risk of accidents based on each company's safe driving data and propose preventive measures. In this way, the logistics efficiency system can promote safe driving and reduce the risk of accidents.

[0041] The logistics efficiency system can further include a customer feedback unit. The customer feedback unit collects feedback from customers after delivery is completed and uses it to improve services. For example, it collects evaluations of the speed of delivery and the driver's response. Customer feedback can also be shared on the cloud to visualize overall service quality. Furthermore, the customer feedback unit can propose specific measures to improve services based on the collected feedback. This allows the logistics efficiency system to improve customer satisfaction and service quality.

[0042] The logistics efficiency system can also be equipped with an environmental monitoring unit. The environmental monitoring unit monitors the exhaust gas and noise levels of each delivery vehicle in real time, promoting a reduction in environmental impact. For example, it can make maintenance suggestions for vehicles with high exhaust gas levels. It can also suggest adjustments to delivery times in areas with high noise levels. Furthermore, the environmental monitoring unit can share each company's environmental data on the cloud and visualize the overall environmental impact. This allows the logistics efficiency system to aim to reduce environmental impact and achieve sustainable logistics.

[0043] The logistics efficiency system can also be equipped with a preventive maintenance unit. The preventive maintenance unit monitors the status of each delivery vehicle and warehouse equipment in real time and detects signs of malfunction. For example, it analyzes vehicle engine noise and vibration data to identify parts that are likely to malfunction. It can also analyze temperature and humidity data for warehouse equipment and suggest maintenance before an abnormality occurs. Furthermore, the preventive maintenance unit can share each company's maintenance data on the cloud and visualize the overall maintenance status. This allows the logistics efficiency system to prevent malfunctions and reduce maintenance costs.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The demand forecasting department uses generation AI to make demand forecasts. The generation AI analyzes data such as past logistics data, market trends, and seasonal fluctuations to predict future demand. It can also predict the amount of logistics demand that will occur in which area at a specific time. The generation AI makes demand forecasts using deep learning models and natural language generation technology. Step 2: The infrastructure sharing unit will use cloud technology to share logistics infrastructure. Logistics data will be aggregated on the cloud, allowing each company to access it in real time. Data shared on the cloud will include delivery routes, delivery schedules, inventory information, and more. Cloud technology will be implemented using a variety of cloud service providers and cloud infrastructure. Step 3: The delivery route optimization unit uses generation AI to generate the optimal delivery route. The generation AI analyzes demand forecast data, real-time traffic information, and delivery destination location information to propose the optimal delivery route. This enables the logistics efficiency system to achieve logistics efficiency and cost reduction.

[0046] (Example 2) The logistics efficiency system according to an embodiment of the present invention utilizes AI prediction and cloud technology to improve the efficiency of logistics. This system reduces the base maintenance and investment costs incurred when each delivery company independently secures its own logistics network, and suppresses increases in transportation costs by allowing the shared use of logistics infrastructure. As a result, the logistics efficiency system can improve logistics efficiency and reduce costs.

[0047] A logistics efficiency improvement system according to an embodiment includes a demand forecasting unit, an infrastructure sharing unit, and a delivery route optimization unit. The demand forecasting unit performs demand forecasting using a generation AI. For example, the generation AI analyzes data such as past logistics data, market trends, and seasonal fluctuations to predict future demand. The generation AI can also predict the amount of logistics demand that will occur in a particular region at a specific time. The generation AI performs demand forecasting using, for example, a deep learning model or natural language generation technology. The infrastructure sharing unit uses cloud technology to share logistics infrastructure. For example, logistics data is aggregated on the cloud so that each company can access it in real time. The data shared on the cloud includes delivery routes, delivery schedules, inventory information, and the like. Cloud technology is realized using, for example, a cloud service provider and a type of cloud infrastructure. The delivery route optimization unit generates an optimal delivery route using the generation AI. For example, the generation AI analyzes demand forecast data, real-time traffic information, location information of delivery destinations, and the like to propose an optimal delivery route. The generation AI generates an optimal delivery route based on, for example, the demand forecast data, real-time traffic information, and location information of delivery destinations. As a result, the logistics efficiency system can achieve logistics efficiency and cost reduction.

[0048] The demand forecasting unit can identify factors that cause demand fluctuations and dynamically adjust the forecasting model based on those factors. The demand forecasting unit, for example, uses a generation AI to identify factors that cause demand fluctuations in addition to the demand forecast. For example, it analyzes data such as weather, economic indicators, and consumer behavior to identify factors that cause demand fluctuations. This allows the demand forecasting model to be dynamically adjusted, resulting in more accurate forecasts. In addition, when the generation AI performs demand forecasting, it identifies factors that cause demand fluctuations and dynamically adjusts the forecasting model based on those factors. For example, it analyzes the impact of specific events and promotions on demand and reflects this in the forecasting model. In addition to the demand forecasting, the generation AI also identifies factors that cause demand fluctuations and dynamically adjusts the forecasting model based on those factors. For example, it takes seasonal fluctuations and market trends into account and updates the demand forecasting model in real time. This improves the accuracy of demand forecasting.

[0049] The demand forecasting unit can analyze external data such as social media or news articles to predict sudden fluctuations in demand. For example, when the generation AI performs demand forecasting, the demand forecasting unit analyzes external data such as social media and news articles to predict sudden fluctuations in demand. For example, if there is a sudden increase in buzz about a particular product, it predicts the possibility of a sudden increase in demand for that product. External data such as social media and news articles is also input into the generation AI to predict sudden fluctuations in demand. For example, the impact of natural disasters or political events on demand is analyzed and reflected in the prediction model. Furthermore, when the generation AI performs demand forecasting, it analyzes external data such as social media and news articles to predict sudden fluctuations in demand. For example, the impact of specific events or campaigns on demand is analyzed in real time and the prediction model is updated. This makes it possible to respond to sudden fluctuations in demand.

[0050] The demand forecasting unit can analyze consumer emotional data and predict the impact of emotional fluctuations on demand. The demand forecasting unit, for example, uses an emotion estimation function to analyze consumer emotional data and predict the impact of emotional fluctuations on demand. For example, it identifies products for which demand increases when consumers' positive emotions are high. It also inputs consumer emotional data into a generation AI to predict the impact of emotional fluctuations on demand. For example, it analyzes the impact of specific events or news on consumer emotions and reflects this in a demand forecasting model. It also uses the emotion estimation function to analyze consumer emotional data and predict the impact of emotional fluctuations on demand. For example, it identifies products for which demand decreases when consumers' emotions are negative and adjusts the forecasting model. This makes it possible to forecast demand based on consumer emotional fluctuations.

[0051] The infrastructure sharing unit visualizes the real-time usage status of each company's logistics resources, enabling efficient resource allocation. For example, in addition to sharing data on the cloud, the infrastructure sharing unit builds a system that visualizes the real-time usage status of each company's logistics resources (for example, warehouses and vehicles). For example, it displays the availability of each company's warehouses and the operating status of their vehicles in real time. It also visualizes the real-time usage status of each company's logistics resources on the cloud and allocates resources efficiently. For example, it allocates resources preferentially to areas where demand is high. In addition to sharing data on the cloud, it visualizes the real-time usage status of each company's logistics resources and allocates resources efficiently. For example, it can grasp resource surpluses and shortages in real time and allocate resources appropriately. This enables efficient allocation of logistics resources.

[0052] The infrastructure sharing unit can integrate the logistics data of each company and provide a dashboard for optimizing overall logistics efficiency. The infrastructure sharing unit, for example, uses cloud technology to integrate the logistics data of each company and provide a dashboard for optimizing overall logistics efficiency. For example, it centrally manages each company's delivery routes and inventory status. It also integrates each company's logistics data on the cloud and provides a dashboard for optimizing overall logistics efficiency. For example, it proposes optimal delivery routes based on real-time data. It also uses cloud technology to integrate the logistics data of each company and provide a dashboard for optimizing overall logistics efficiency. For example, it visualizes each company's resource usage status and allocates resources efficiently. This makes it possible to optimize overall logistics efficiency.

[0053] The infrastructure sharing unit can collect employee emotional data and use it to improve the working environment and increase motivation. For example, the infrastructure sharing unit uses an emotion estimation function to collect employee emotional data and use it to improve the working environment and increase motivation. For example, it monitors employee stress levels and takes appropriate measures. It also collects employee emotional data on the cloud and uses it to improve the working environment and increase motivation. For example, it identifies areas for improvement in the working environment based on the emotional data. It also uses the emotion estimation function to collect employee emotional data and use it to improve the working environment and increase motivation. For example, it implements measures to improve motivation based on employee emotional data. This makes it possible to improve the working environment and increase motivation.

[0054] The delivery route optimization unit can dynamically recalculate the route in response to real-time changes in the situation. For example, using a generation AI, the delivery route optimization unit dynamically recalculates the route in response to real-time changes in the situation during delivery (for example, traffic congestion or weather) in addition to optimizing the delivery route. For example, if a traffic congestion occurs, it proposes an optimal detour route. In addition, real-time changes in the situation during delivery are input into the generation AI, which dynamically recalculates the route. For example, it proposes a safe route in response to changes in the weather. In addition to optimizing the delivery route, the generation AI dynamically recalculates the route in response to real-time changes in the situation during delivery. For example, it proposes an optimal route based on accident or construction information. This makes it possible to optimize delivery routes in response to real-time changes in the situation.

[0055] The delivery route optimization unit can optimize routes taking into account energy consumption or environmental load. For example, the delivery route optimization unit adds optimization that takes into account energy consumption and environmental load to the delivery route proposed by the generation AI. For example, it may prioritize routes that consume less fuel. It also inputs energy consumption and environmental load into the generation AI to propose the optimal delivery route. For example, it may select a route with low CO2 emissions. It also adds optimization that takes into account energy consumption and environmental load to the delivery route proposed by the generation AI. For example, it may propose a route that encourages eco-driving. This makes it possible to optimize delivery routes taking into account energy consumption and environmental load.

[0056] The delivery route optimization unit can analyze the driver's emotional data and suggest a route with less stress. The delivery route optimization unit, for example, uses an emotion estimation function to analyze the driver's emotional data and suggest a route with less stress. For example, it selects a route with less traffic. The driver's emotional data is also input into a generation AI to suggest a route with less stress. For example, it suggests a route with beautiful scenery or many rest stops. The emotion estimation function can also be used to analyze the driver's emotional data and suggest a route with less stress. For example, it suggests a route that allows the driver to relax if their emotions are negative. This makes it possible to suggest delivery routes that reduce driver stress.

[0057] The delivery route optimization unit can simultaneously optimize the schedule in addition to optimizing the route. For example, the delivery route optimization unit uses generation AI to simultaneously optimize not only the delivery route but also the delivery schedule. For example, it proposes the optimal delivery time based on demand forecast data. In addition, the optimized delivery route and schedule are input into the generation AI to propose an efficient delivery plan. For example, it sets a schedule that matches the business hours of the delivery destination. In addition, the generation AI is used to simultaneously optimize not only the delivery route but also the delivery schedule. For example, it proposes the optimal delivery time to avoid traffic congestion. This allows the delivery route and schedule to be optimized simultaneously.

[0058] The delivery route optimization unit generates a maintenance schedule based on the optimized route, thereby improving vehicle utilization rates. The delivery route optimization unit, for example, uses a generation AI to generate a vehicle maintenance schedule based on the optimized delivery route. For example, it plans maintenance after long-distance driving. It also generates a vehicle maintenance schedule based on the optimized delivery route data. For example, it grasps the vehicle's operating status in real time and proposes appropriate maintenance timing. It also uses a generation AI to generate a vehicle maintenance schedule based on the optimized delivery route, thereby improving vehicle utilization rates. For example, it optimizes the timing of maintenance and minimizes vehicle downtime. This makes it possible to generate a maintenance schedule that improves vehicle utilization rates.

[0059] The delivery route optimization unit can monitor customer satisfaction with delivery in real time and use the information to improve services. The delivery route optimization unit, for example, uses an emotion estimation function to monitor customer satisfaction with delivery in real time and use the information to improve services. For example, it analyzes the customer's facial expressions and voice to evaluate satisfaction. It also monitors customer satisfaction with delivery in real time on the cloud and uses the information to improve services. For example, it identifies areas for improvement in delivery services based on emotion data. It also uses the emotion estimation function to monitor customer satisfaction with delivery in real time and use the information to improve services. For example, it adjusts delivery routes and schedules based on customer emotion data. This allows customer satisfaction to be monitored in real time and used to improve services.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The logistics efficiency system can further be equipped with an energy management unit. The energy management unit monitors the fuel and power consumption of each delivery vehicle in real time and promotes efficient energy use. For example, it can provide eco-driving guidance to vehicles with high fuel consumption. It can also suggest the introduction of energy-efficient equipment to warehouses with high power consumption. Furthermore, the energy management unit can share each company's energy usage data on the cloud and provide a dashboard for optimizing overall energy efficiency. This allows the logistics efficiency system to reduce energy consumption and the environmental impact.

[0062] The logistics efficiency system can also be equipped with a safety management department. The safety management department monitors the operating status of each delivery vehicle in real time and promotes safe driving. For example, it analyzes the frequency of sudden braking and sudden acceleration and provides guidance on safe driving. It can also share driver driving status on the cloud and visualize the overall safe driving status. Furthermore, the safety management department can identify routes and time periods with a high risk of accidents based on each company's safe driving data and propose preventive measures. In this way, the logistics efficiency system can promote safe driving and reduce the risk of accidents.

[0063] The logistics efficiency system can further include a customer feedback unit. The customer feedback unit collects feedback from customers after delivery is completed and uses it to improve services. For example, it collects evaluations of the speed of delivery and the driver's response. Customer feedback can also be shared on the cloud to visualize overall service quality. Furthermore, the customer feedback unit can propose specific measures to improve services based on the collected feedback. This allows the logistics efficiency system to improve customer satisfaction and service quality.

[0064] The logistics efficiency system can also be equipped with an environmental monitoring unit. The environmental monitoring unit monitors the exhaust gas and noise levels of each delivery vehicle in real time, promoting a reduction in environmental impact. For example, it can make maintenance suggestions for vehicles with high exhaust gas levels. It can also suggest adjustments to delivery times in areas with high noise levels. Furthermore, the environmental monitoring unit can share each company's environmental data on the cloud and visualize the overall environmental impact. This allows the logistics efficiency system to aim to reduce environmental impact and achieve sustainable logistics.

[0065] The logistics efficiency system can also be equipped with a preventive maintenance unit. The preventive maintenance unit monitors the status of each delivery vehicle and warehouse equipment in real time and detects signs of malfunction. For example, it analyzes vehicle engine noise and vibration data to identify parts that are likely to malfunction. It can also analyze temperature and humidity data for warehouse equipment and suggest maintenance before an abnormality occurs. Furthermore, the preventive maintenance unit can share each company's maintenance data on the cloud and visualize the overall maintenance status. This allows the logistics efficiency system to prevent malfunctions and reduce maintenance costs.

[0066] The logistics efficiency system can also use the emotion estimation function to analyze customer emotion data and provide services that meet the customer's needs. For example, if a customer is dissatisfied with a delivery, the system can identify the cause and propose improvements. It can also provide customized services that meet individual needs based on the customer's emotion data. Furthermore, the emotion estimation function can be used to analyze customer emotion data and aim to improve customer satisfaction. For example, if a customer has positive emotions, the system can implement measures to maintain those emotions. This allows the logistics efficiency system to provide services based on the customer's emotions.

[0067] The logistics efficiency system can also use the emotion estimation function to analyze the driver's emotional data and implement measures to reduce the driver's stress. For example, if the driver is feeling stressed, the cause can be identified and appropriate measures can be taken. It can also suggest routes and schedules that are less stressful based on the driver's emotional data. Furthermore, the emotion estimation function can be used to analyze the driver's emotional data and aim to improve the driver's motivation. For example, if the driver has positive emotions, measures can be implemented to maintain those emotions. In this way, the logistics efficiency system can realize operation management based on the driver's emotions.

[0068] The logistics efficiency system can also use the emotion estimation function to analyze employee emotional data and use this information to improve the working environment and increase motivation. For example, if an employee is feeling stressed, the cause can be identified and appropriate measures can be taken. Furthermore, based on employee emotional data, areas for improvement in the working environment can be identified and specific measures can be proposed. Furthermore, the emotion estimation function can be used to analyze employee emotional data and aim to improve employee motivation. For example, if an employee has positive emotions, measures can be implemented to maintain those emotions. In this way, the logistics efficiency system can improve the working environment based on employee emotions.

[0069] The logistics efficiency system can also use the emotion estimation function to analyze customer emotion data and use the data to optimize marketing strategies. For example, if customers have positive feelings about a particular product, the system can strengthen promotion of that product. Targeted marketing can also be carried out based on customer emotion data. Furthermore, the emotion estimation function can be used to analyze customer emotion data and evaluate the effectiveness of marketing strategies. For example, the impact of a particular campaign on customer emotions can be analyzed and reflected in the next campaign. This allows the logistics efficiency system to optimize marketing strategies based on customer emotions.

[0070] The logistics efficiency system can further use the emotion estimation function to analyze the driver's emotional data and use this information to optimize the operation schedule. For example, if the driver is feeling tired, the system can adjust the driver's rest time based on that emotional data. It can also propose a less stressful operation schedule based on the driver's emotional data. Furthermore, the emotion estimation function can be used to analyze the driver's emotional data and aim to improve the efficiency of the operation schedule. For example, if the driver has positive emotions, the system can set a schedule to maintain that emotion. In this way, the logistics efficiency system can optimize the operation schedule based on the driver's emotions.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The demand forecasting department uses generation AI to make demand forecasts. The generation AI analyzes data such as past logistics data, market trends, and seasonal fluctuations to predict future demand. It can also predict the amount of logistics demand that will occur in which area at a specific time. The generation AI makes demand forecasts using deep learning models and natural language generation technology. Step 2: The infrastructure sharing unit will use cloud technology to share logistics infrastructure. Logistics data will be aggregated on the cloud, allowing each company to access it in real time. Data shared on the cloud will include delivery routes, delivery schedules, inventory information, and more. Cloud technology will be implemented using a variety of cloud service providers and cloud infrastructure. Step 3: The delivery route optimization unit uses generation AI to generate the optimal delivery route. The generation AI analyzes demand forecast data, real-time traffic information, and delivery destination location information to propose the optimal delivery route. This enables the logistics efficiency system to achieve logistics efficiency and cost reduction.

[0073] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0074] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0075] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0078] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0079] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0080] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0081] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0082] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0083] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0084] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0085] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0086] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0087] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0088] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0089] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0090] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0093] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0094] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0095] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0096] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0097] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0098] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0099] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0101] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0102] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0103] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 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.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0114] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0123] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0124] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0125] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0126] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0127] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0128] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0129] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0130] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0131] 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.

[0132] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0133] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0134] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0135] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0136] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0137] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0138] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0139] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0140] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a demand forecasting unit that uses generation AI to make demand forecasts; An infrastructure sharing department that uses cloud technology to share logistics infrastructure, A delivery route optimization unit that generates an optimal delivery route using a generation AI. A system characterized by:

2. The demand forecasting unit Identify drivers of demand fluctuations and dynamically adjust forecasting models based on those drivers 2. The system of claim 1.

3. The demand forecasting unit Analyze external data from social media or news articles to predict sudden demand fluctuations 2. The system of claim 1.

4. The demand forecasting unit Analyzing consumer sentiment data and predicting the impact of sentiment fluctuations on demand 2. The system of claim 1.

5. The infrastructure sharing unit includes: Visualize the real-time utilization of logistics resources of each company and allocate resources efficiently 2. The system of claim 1.

6. The infrastructure sharing unit includes: Integrates logistics data from each company and provides a dashboard to optimize overall logistics efficiency 2. The system of claim 1.

7. The infrastructure sharing unit includes: Collect employee emotional data to improve the working environment and increase motivation 2. The system of claim 1.

8. The delivery route optimization unit Dynamically recalculate routes based on real-time changes 2. The system of claim 1.

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