System

Abstract

An object of the system according to the embodiment is to bring the lead time of physical distribution infinitely close to zero through demand prediction and optimization of a physical distribution route.SOLUTION: A system according to an embodiment includes a collection unit, a prediction unit, a route calculation unit, an inventory arrangement unit, a monitoring unit, and a re-calculation unit. The collection unit collects demand data. The prediction unit performs demand prediction based on the data collected by the collection unit. The route calculation unit calculates an efficient physical distribution route based on the demand predicted by the prediction unit. The stock arrangement unit optimizes the arrangement of the stock based on the route calculated by the route calculation unit. The monitoring unit monitors in real time the progress of the inventory placed by the inventory placement unit. The recalculation unit performs recalculation of the route and rearrangement of the inventory based on the progress status monitored by the monitoring unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately forecast demand or optimize logistics routes, leaving room for improvement in terms of shortening logistics lead times.

[0005] The system according to the embodiment aims to reduce logistics lead times to as close to zero as possible through demand forecasting and optimization of logistics routes. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a prediction unit, a route calculation unit, an inventory allocation unit, a monitoring unit, and a recalculation unit. The collection unit collects demand data. The prediction unit performs demand forecasting based on the data collected by the collection unit. The route calculation unit calculates an efficient logistics route based on the demand predicted by the prediction unit. The inventory allocation unit improves the efficiency of inventory allocation based on the route calculated by the route calculation unit. The monitoring unit monitors the progress of inventory allocated by the inventory allocation unit in real time. The recalculation unit recalculates the route and reallocates inventory based on the progress monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce logistics lead times to as close to zero as possible through demand forecasting and optimization of logistics routes. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A logistics optimization system according to an embodiment of the present invention collects demand data, forecasts demand using AI, calculates optimal logistics routes, and optimizes inventory allocation. The logistics optimization system collects demand data, forecasts demand using AI, calculates optimal logistics routes, and optimizes inventory allocation. Furthermore, the logistics optimization system monitors the progress of logistics in real time and recalculates routes and reallocates inventory as necessary. For example, the logistics optimization system collects demand data for each region, including past sales data and seasonal demand fluctuations. The logistics optimization system then inputs the collected data into AI to perform a demand forecast. The AI ​​analyzes the past data and predicts future demand. The logistics optimization system then calculates optimal logistics routes based on the predicted demand. The AI ​​then proposes optimal routes taking into account traffic conditions and inventory allocation. The logistics optimization system then optimizes inventory allocation. For example, the system allocates more inventory in areas with high demand and less inventory in areas with low demand. The logistics optimization system then monitors the progress of logistics in real time. For example, the system uses GPS to obtain truck location information and grasps the progress of logistics. Next, the logistics optimization system recalculates routes and reallocates inventory as necessary. For example, if a traffic jam occurs, the AI ​​calculates a new route and issues instructions to trucks. In this way, the logistics optimization system can reduce logistics lead times to as close to zero as possible. This allows the logistics optimization system to significantly shorten the time it takes for products to reach customers. For example, customers can receive products the moment they want them, improving satisfaction. Companies can also expect to see reductions in inventory management and logistics costs.

[0029] A logistics optimization system according to an embodiment includes a collection unit, a prediction unit, a route calculation unit, an inventory allocation unit, a monitoring unit, and a recalculation unit. The collection unit collects demand data. Demand data includes, but is not limited to, sales data, customer behavior data, and market trend data. The collection unit collects, for example, past sales data. The collection unit can also collect seasonal demand fluctuation data. For example, the collection unit collects daily sales data and monthly sales data. The prediction unit performs demand forecasting based on the data collected by the collection unit. The prediction unit performs demand forecasting using, for example, time series analysis, regression analysis, or a machine learning model. For example, the prediction unit analyzes past data and predicts future demand. The route calculation unit calculates an optimal logistics route based on the demand predicted by the prediction unit. The route calculation unit calculates the optimal route taking into account, for example, traffic conditions and inventory allocation status. For example, the route calculation unit calculates a route based on minimizing distance, time, and cost. The inventory allocation unit optimizes inventory allocation based on the route calculated by the route calculation unit. For example, the inventory allocation unit allocates more inventory in areas with high demand and less inventory in areas with low demand. For example, the inventory allocation unit allocates inventory based on a demand forecast. The monitoring unit monitors the progress of the inventory allocated by the inventory allocation unit in real time. For example, the monitoring unit obtains truck location information using GPS and grasps the progress of logistics. For example, the monitoring unit monitors the progress based on the real-time location information. The recalculation unit recalculates routes and relocates inventory based on the progress monitored by the monitoring unit. For example, the recalculation unit calculates a new route when traffic congestion occurs and issues instructions to the truck. For example, the recalculation unit recalculates routes in response to changes in traffic conditions. As a result, the logistics optimization system according to the embodiment can reduce logistics lead times to as close to zero as possible by consistently performing processes from demand data collection to forecasting, route calculation, inventory allocation, progress monitoring, and recalculation.

[0030] The collection unit can collect past sales data or data on seasonal demand fluctuations. The collection unit, for example, collects past sales data. Past sales data includes, but is not limited to, daily sales data, monthly sales data, and product-specific sales data. The collection unit can also collect seasonal demand fluctuation data. For example, the collection unit collects seasonal sales data and the effects of seasonal events. This enables more accurate demand forecasting by taking past sales data and seasonal demand fluctuations into account. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past sales data into AI and have the AI ​​collect the data.

[0031] The prediction unit can analyze the collected data and predict future demand. The prediction unit, for example, analyzes the collected data and predicts future demand. The collected data includes, but is not limited to, sales data, customer behavior data, and market trend data. The prediction unit performs demand prediction using, for example, time series analysis, regression analysis, or a machine learning model. For example, the prediction unit analyzes past data and predicts future demand. In this way, future demand can be accurately predicted by analyzing the collected data. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the collected data into AI and have the AI ​​perform demand prediction.

[0032] The route calculation unit can calculate an optimal logistics route by taking into account traffic conditions and inventory distribution conditions. The route calculation unit calculates the optimal logistics route by taking into account, for example, traffic conditions and inventory distribution conditions. Examples of traffic conditions include, but are not limited to, real-time traffic information and past traffic data. Examples of inventory distribution conditions include, but are not limited to, the amount of inventory in each warehouse and the location of products. The route calculation unit calculates a route based on, for example, minimizing distance, minimizing time, and optimizing cost. For example, the route calculation unit proposes an optimal route by taking into account traffic conditions and inventory distribution conditions. This makes it possible to calculate an optimal logistics route by taking into account traffic conditions and inventory distribution conditions. Some or all of the above-described processing in the route calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the route calculation unit may input data on traffic conditions and inventory distribution conditions into AI and have the AI ​​calculate the optimal route.

[0033] The inventory allocation unit can allocate more inventory in areas with high demand and less inventory in areas with low demand. For example, the inventory allocation unit allocates more inventory in areas with high demand and less inventory in areas with low demand. Areas with high demand include, but are not limited to, past sales data and demand forecasts for each area. Areas with low demand include, but are not limited to, past sales data and demand forecasts for each area. This enables inventory optimization by allocating inventory according to demand. Some or all of the above-described processing in the inventory allocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the inventory allocation unit may input demand forecast data into AI and have the AI ​​execute inventory allocation optimization.

[0034] The monitoring unit can obtain truck location information using GPS and grasp the progress of logistics. The monitoring unit, for example, obtains truck location information using GPS and grasps the progress of logistics. The progress of logistics includes, for example, real-time location information and inventory consumption status, but is not limited to these examples. The monitoring unit, for example, monitors the progress based on real-time location information. In this way, by using GPS, the location information of the truck can be grasped in real time. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input GPS data into AI and have the AI ​​monitor the progress.

[0035] The recalculation unit can calculate a new route when a traffic jam occurs and issue instructions to the truck. For example, the recalculation unit calculates a new route when a traffic jam occurs and issues instructions to the truck. Traffic jams include, but are not limited to, real-time traffic information and past traffic data. For example, the recalculation unit recalculates the route in accordance with changes in traffic conditions. This makes it possible to quickly calculate a new route and issue instructions to the truck even when a traffic jam occurs. Some or all of the above-mentioned processing in the recalculation unit may be performed using, or without, an AI. For example, the recalculation unit can input traffic jam data into an AI and have the AI ​​calculate a new route.

[0036] The collection unit can collect weather data and event information in addition to past sales data and seasonal demand fluctuations. For example, the collection unit collects weather data and event information in addition to past sales data and seasonal demand fluctuations. Weather data includes, but is not limited to, data from the Japan Meteorological Agency and real-time weather information. Event information includes, but is not limited to, a local event calendar and past event data. For example, the collection unit collects weather data in addition to past sales data and reflects it in a demand forecast. The collection unit can also collect local event information in addition to seasonal demand fluctuations and use it in a demand forecast. The collection unit can also integrate past sales data, weather data, and event information to perform more accurate demand forecasts. Collecting weather data and event information thereby enables more accurate demand forecasts. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input weather data and event information into AI and have the AI ​​collect the data.

[0037] The collection unit can evaluate the reliability of data at the time of collection and exclude unreliable data. For example, the collection unit evaluates the reliability of data at the time of collection and excludes unreliable data. Data reliability includes, but is not limited to, the reliability of the data source and the consistency of the data. For example, the collection unit uses AI to evaluate the reliability of the collected data and automatically exclude unreliable data. The collection unit can also cross-check multiple data sources to evaluate the reliability of the data. The collection unit can also evaluate the source of the data and the collection method to exclude unreliable data. In this way, by evaluating the reliability of the data, unreliable data can be excluded and highly accurate predictions can be made. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can have AI perform the data reliability evaluation.

[0038] The collection unit can detect duplicate data during collection and integrate the duplicate data. For example, the collection unit can detect duplicate data during collection and integrate the duplicate data. Examples of data duplication include, but are not limited to, a database duplication check algorithm. For example, the collection unit can use AI to detect duplicate data and automatically integrate the collected data. The collection unit can also evaluate the uniqueness of the data to detect duplicate data. The collection unit can also compare the contents of the data and select the most reliable data to integrate the duplicate data. This allows the detection and integration of duplicate data to maintain data consistency. Some or all of the above-described processing in the collection unit can be performed using AI, or can be performed without AI. For example, the collection unit can have AI perform the detection and integration of duplicate data.

[0039] The collection unit can collect social media trend data during collection and use it for demand forecasting. For example, the collection unit collects social media trend data during collection and uses it for demand forecasting. Social media trend data includes, but is not limited to, hashtag frequency analysis and post content analysis. For example, the collection unit collects social media trend data and reflects it in demand forecasting. The collection unit can also analyze social media post content to predict demand fluctuations. The collection unit can also integrate social media trend data with other data to perform more accurate demand forecasting. In this way, collecting social media trend data improves the accuracy of demand forecasting. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media trend data into AI and have the AI ​​collect data.

[0040] The collection unit may collect competitors' sales data and perform comparative analysis during collection. For example, the collection unit may collect competitors' sales data and perform comparative analysis during collection. Examples of competitors' sales data include, but are not limited to, market research reports and publicly available sales data. For example, the collection unit may collect competitors' sales data and compare it with the company's own sales data. The collection unit may also analyze competitors' sales data and predict demand fluctuations. The collection unit may also integrate competitors' sales data with other data to perform more accurate demand forecasts. This enables more accurate demand forecasts by collecting competitors' sales data and performing comparative analysis. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input competitors' sales data into AI and have the AI ​​perform data collection and analysis.

[0041] The collection unit can collect regional economic indicator data at the time of collection and reflect it in the demand forecast. For example, the collection unit collects regional economic indicator data at the time of collection and reflects it in the demand forecast. Regional economic indicator data includes, but is not limited to, government statistical data and economic reports. For example, the collection unit collects regional economic indicator data and reflects it in the demand forecast. The collection unit can also analyze the economic indicator data and predict demand fluctuations. The collection unit can also integrate the economic indicator data with other data to perform more accurate demand forecasts. In this way, collecting regional economic indicator data improves the accuracy of the demand forecast. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input regional economic indicator data into AI and have the AI ​​collect and analyze the data.

[0042] The prediction unit can improve prediction accuracy by combining different prediction algorithms during prediction. The prediction unit, for example, improves prediction accuracy by combining different prediction algorithms during prediction. Different prediction algorithms include, but are not limited to, a combination of time series analysis and a machine learning model. The prediction unit, for example, combines different prediction algorithms to improve prediction accuracy. The prediction unit can also compare multiple prediction algorithms and select the algorithm with the highest accuracy. The prediction unit can also combine different prediction algorithms to improve the reliability of the prediction results. As a result, combining different prediction algorithms improves prediction accuracy. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input different prediction algorithms into AI and have the AI ​​improve prediction accuracy.

[0043] The prediction unit can compare past prediction results with actual demand during prediction and improve the prediction model. For example, the prediction unit compares past prediction results with actual demand during prediction and improves the prediction model. Past prediction results include, but are not limited to, past sales prediction data and actual sales data. For example, the prediction unit compares past prediction results with actual demand and improves the prediction model. The prediction unit can also analyze errors in the prediction results and improve the accuracy of the prediction model. The prediction unit can also improve the reliability of the prediction model by comparing past prediction results with actual demand. Comparing past prediction results with actual demand improves the accuracy of the prediction model. Some or all of the above-described processing in the prediction unit can be performed using, for example, AI, or without AI. For example, the prediction unit can input past prediction results and actual demand data into AI and have the AI ​​improve the prediction model.

[0044] The prediction unit can detect a sudden change in demand during prediction and issue an alert. For example, the prediction unit detects a sudden change in demand during prediction and issues an alert. Examples of sudden changes in demand include, but are not limited to, an anomaly detection algorithm and threshold setting. For example, the prediction unit uses AI to detect a sudden change in demand and issue an alert. The prediction unit can also monitor real-time data to detect a sudden change in demand. The prediction unit can also detect a sudden change in demand and propose an appropriate response. This enables a rapid response by detecting a sudden change in demand and issuing an alert. Some or all of the above-mentioned processing in the prediction unit may be performed using AI, for example, or may be performed without using AI. For example, the prediction unit can input data on sudden changes in demand into AI and have the AI ​​detect the change and issue an alert.

[0045] The prediction unit can perform demand prediction on different time axes during prediction. The prediction unit, for example, performs demand prediction on different time axes during prediction. Different time axes include, but are not limited to, short-term (one week), medium-term (one month), and long-term (one year) periods. The prediction unit, for example, performs short-term demand prediction and proposes immediate responses. The prediction unit can also perform medium-term demand prediction and propose planned responses. The prediction unit can also perform long-term demand prediction and propose strategic responses. In this way, by performing demand prediction on different time axes, planning from short-term to long-term becomes possible. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input data on different time axes into AI and have the AI ​​perform demand prediction.

[0046] The forecasting unit can perform demand forecasts for different regions and reflect regional characteristics during forecasting. For example, the forecasting unit performs demand forecasts for different regions and reflects regional characteristics during forecasting. Examples of different regions include, but are not limited to, urban areas, suburban areas, and rural areas. For example, the forecasting unit can perform demand forecasts for different regions and reflect regional characteristics. Furthermore, the forecasting unit can also propose optimal inventory allocations based on the demand forecasts for each region. Furthermore, the forecasting unit can perform demand forecasts that take regional characteristics into account, thereby improving accuracy. By performing demand forecasts that reflect regional characteristics, optimal responses for each region are possible. Some or all of the above-described processing in the forecasting unit may be performed using, for example, AI, or may be performed without using AI. For example, the forecasting unit can input data for each region into AI and have the AI ​​perform demand forecasts.

[0047] The prediction unit can perform demand forecasts for different product categories during prediction. For example, the prediction unit performs demand forecasts for different product categories during prediction. Different product categories include, but are not limited to, food, clothing, and electronic devices. For example, the prediction unit performs demand forecasts for different product categories and reflects category characteristics. The prediction unit can also propose optimal inventory allocation based on the demand forecast for each product category. The prediction unit can also perform demand forecasts that take product category characteristics into account to improve accuracy. As a result, performing demand forecasts for each product category enables responses that reflect category characteristics. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input data for each product category into AI and have the AI ​​perform demand forecasts.

[0048] The route calculation unit can optimize the route by taking into account the risk of traffic accidents and natural disasters when calculating the route. The route calculation unit, for example, optimizes the route by taking into account the risk of traffic accidents and natural disasters when calculating the route. Traffic accidents include, for example, real-time traffic accident data and past accident data, but are not limited to these examples. Risks of natural disasters include, for example, weather data and earthquake data, but are not limited to these examples. The route calculation unit, for example, proposes an optimal route by taking into account the risk of traffic accidents. The route calculation unit can also propose an optimal route by taking into account the risk of natural disasters. The route calculation unit can also propose an optimal route by integrating the risks of traffic accidents and natural disasters. This makes it possible to propose a safer route by taking into account the risk of traffic accidents and natural disasters. Some or all of the above-described processing in the route calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the route calculation unit can input risk data of traffic accidents and natural disasters into AI and have the AI ​​calculate the optimal route.

[0049] The route calculation unit can propose an optimal route by combining multiple delivery means when calculating a route. For example, the route calculation unit proposes an optimal route by combining multiple delivery means (truck, rail, ship, etc.) when calculating a route. Examples of multiple delivery means include, but are not limited to, truck, rail, ship, and air. For example, the route calculation unit proposes an optimal route by combining truck and rail. The route calculation unit can also propose an optimal route by combining truck and ship. The route calculation unit can also propose an optimal route by combining multiple delivery means. In this way, an optimal route can be proposed by combining multiple delivery means. Some or all of the above-described processing in the route calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the route calculation unit can input data on multiple delivery means into AI and have the AI ​​calculate the optimal route.

[0050] The route calculation unit may calculate a route that minimizes energy consumption during route calculation. For example, the route calculation unit may calculate a route that minimizes energy consumption during route calculation. Energy consumption may include, but is not limited to, fuel consumption and power consumption. For example, the route calculation unit may propose an optimal route to minimize energy consumption. The route calculation unit may also compare multiple routes and select the optimal route, taking energy consumption into consideration. The route calculation unit may also consider real-time traffic information to minimize energy consumption. By minimizing energy consumption, this reduces environmental impact and costs. Some or all of the above-described processing in the route calculation unit may be performed using, or without, AI. For example, the route calculation unit may input energy consumption data into AI and have the AI ​​calculate the optimal route.

[0051] The route calculation unit can optimize the route by taking into account the available pickup times at the delivery destinations when calculating the route. For example, the route calculation unit optimizes the route by taking into account the available pickup times at the delivery destinations when calculating the route. The available pickup times at the delivery destinations include, but are not limited to, the customer's desired time slot and past pickup history. The route calculation unit, for example, proposes an optimal route by taking into account the available pickup times at the delivery destinations. The route calculation unit can also calculate a route that efficiently travels around multiple delivery destinations with different available pickup times. The route calculation unit can also optimize the delivery order by taking into account the available pickup times. This enables efficient delivery by taking into account the available pickup times at the delivery destinations. Some or all of the above-described processing in the route calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the route calculation unit can input data on the available pickup times into AI and have the AI ​​calculate the optimal route.

[0052] The route calculation unit can optimize the route by taking into account the geographical conditions of the delivery destination when calculating the route. For example, the route calculation unit optimizes the route by taking into account the geographical conditions of the delivery destination (mountainous area, urban area, etc.) when calculating the route. Geographical conditions of the delivery destination include, but are not limited to, mountainous area, urban area, and suburban area. For example, the route calculation unit proposes an optimal route by taking into account a delivery destination in a mountainous area. The route calculation unit can also propose an optimal route by taking into account a delivery destination in an urban area. The route calculation unit can also compare multiple routes by taking into account the geographical conditions and select the optimal route. In this way, the optimal route can be proposed by taking the geographical conditions into account. Some or all of the above-described processing in the route calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the route calculation unit can input data on geographical conditions into AI and have the AI ​​calculate the optimal route.

[0053] The route calculation unit can optimize the route by taking into account specific requirements of the delivery destination when calculating the route. For example, the route calculation unit optimizes the route by taking into account specific requirements (e.g., refrigeration, freezing) of the delivery destination when calculating the route. Specific requirements of the delivery destination include, but are not limited to, refrigeration, freezing, and specific temperature control. For example, the route calculation unit proposes an optimal route by taking into account the delivery destination of refrigerated goods. The route calculation unit can also propose an optimal route by taking into account the delivery destination of frozen goods. The route calculation unit can also compare multiple routes by taking into account the specific requirements and select the optimal route. In this way, the optimal route can be proposed by taking the specific requirements into account. Some or all of the above-described processing in the route calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the route calculation unit can input data of the specific requirements into AI and have the AI ​​calculate the optimal route.

[0054] The inventory allocation unit can optimize inventory allocation by taking into account the warehouse capacity and available space. For example, the inventory allocation unit optimizes inventory allocation by taking into account the warehouse capacity and available space. Examples of warehouse capacity include, but are not limited to, the total capacity of the warehouse and the currently used capacity. Examples of available space include, but are not limited to, available shelf space and available pallet space. For example, the inventory allocation unit proposes optimal inventory allocation by taking into account the warehouse capacity. The inventory allocation unit can also optimize inventory allocation by taking into account the available space. The inventory allocation unit can also integrate the warehouse capacity and the available space to propose optimal inventory allocation. This enables efficient inventory allocation by taking into account the warehouse capacity and available space. Some or all of the above-described processing in the inventory allocation unit may be performed using, or without, AI. For example, the inventory allocation unit can input data on the warehouse capacity and available space into AI and have the AI ​​calculate the optimal inventory allocation.

[0055] The inventory allocation unit can optimize inventory allocation by taking into account product storage conditions during inventory allocation. For example, the inventory allocation unit optimizes inventory allocation by taking into account product storage conditions (temperature, humidity, etc.) during inventory allocation. Product storage conditions include, but are not limited to, temperature, humidity, and light conditions. For example, the inventory allocation unit proposes an optimal inventory allocation by taking into account product storage conditions. The inventory allocation unit can also optimize product allocation by taking into account temperature and humidity. The inventory allocation unit can also integrate storage conditions and propose an optimal inventory allocation. This enables optimal inventory allocation by taking into account product storage conditions. Some or all of the above-described processing in the inventory allocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the inventory allocation unit can input data on product storage conditions into AI and have the AI ​​calculate the optimal inventory allocation.

[0056] The inventory allocation unit can optimize inventory allocation by taking product turnover into consideration when allocating inventory. For example, the inventory allocation unit optimizes inventory allocation by taking product turnover into consideration when allocating inventory. Product turnover includes, but is not limited to, monthly sales volume and inventory retention period. For example, the inventory allocation unit proposes optimal inventory allocation by taking product turnover into consideration. The inventory allocation unit can also prioritize the allocation of products with high turnover rates. The inventory allocation unit can also integrate turnover rates to propose optimal inventory allocation. This enables efficient inventory management by taking product turnover into consideration. Some or all of the above-described processing in the inventory allocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the inventory allocation unit can input product turnover data into AI and have the AI ​​calculate the optimal inventory allocation.

[0057] The inventory allocation unit can optimize inventory allocation by taking into account the size and weight of the products when allocating inventory. For example, the inventory allocation unit optimizes inventory allocation by taking into account the size and weight of the products when allocating inventory. Product size includes, but is not limited to, product dimensions and package size. Product weight includes, but is not limited to, the total weight of the products and package weight. For example, the inventory allocation unit proposes optimal inventory allocation by taking into account the size of the products. The inventory allocation unit can also optimize inventory allocation by taking into account the weight of the products. The inventory allocation unit can also integrate size and weight to propose optimal inventory allocation. This enables efficient inventory management by taking into account the size and weight of the products. Some or all of the above-described processing in the inventory allocation unit may be performed using, or without, AI. For example, the inventory allocation unit can input data on the size and weight of products into AI and have the AI ​​calculate the optimal inventory allocation.

[0058] The inventory allocation unit can optimize inventory allocation by taking into account the shipping frequency of the product when allocating inventory. For example, the inventory allocation unit optimizes inventory allocation by taking into account the shipping frequency of the product when allocating inventory. Product shipping frequency includes, but is not limited to, daily shipping volume and monthly shipping volume. For example, the inventory allocation unit proposes an optimal inventory allocation by taking into account the shipping frequency of the product. The inventory allocation unit can also prioritize the allocation of products with high shipping frequencies. The inventory allocation unit can also integrate shipping frequencies to propose an optimal inventory allocation. This enables efficient inventory management by taking into account the shipping frequency of the product. Some or all of the above-described processing in the inventory allocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the inventory allocation unit can input data on the shipping frequency of the product into AI and have the AI ​​calculate the optimal inventory allocation.

[0059] The inventory allocation unit can optimize inventory allocation by taking into account product storage costs when allocating inventory. For example, the inventory allocation unit optimizes inventory allocation by taking into account product storage costs when allocating inventory. Product storage costs include, but are not limited to, warehouse rent and costs according to storage period. For example, the inventory allocation unit proposes optimal inventory allocation by taking into account product storage costs. The inventory allocation unit can also prioritize product allocation with low storage costs. The inventory allocation unit can also integrate storage costs to propose optimal inventory allocation. This enables efficient inventory management by taking product storage costs into account. Some or all of the above-described processing in the inventory allocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the inventory allocation unit can input product storage cost data into AI and have the AI ​​calculate the optimal inventory allocation.

[0060] The monitoring unit can visualize the progress of logistics in real time and detect abnormalities during monitoring. For example, the monitoring unit visualizes the progress of logistics in real time and detects abnormalities during monitoring. Examples of the progress of logistics include, but are not limited to, real-time location information and inventory consumption status. Examples of abnormalities include, but are not limited to, delays from scheduled times and inventory shortages. For example, the monitoring unit visualizes the progress of logistics in real time and detects abnormalities. The monitoring unit can also monitor real-time data to detect abnormalities. The monitoring unit can also detect abnormalities and propose appropriate responses. This enables rapid responses by visualizing the progress of logistics in real time and detecting abnormalities. Some or all of the above-described processing by the monitoring unit may be performed using, for example, AI, or without AI. For example, the monitoring unit can input logistics progress data into AI and have the AI ​​detect abnormalities.

[0061] The monitoring unit can monitor the truck's fuel consumption and driving time during monitoring to support efficient operation. The monitoring unit, for example, monitors the truck's fuel consumption and driving time during monitoring to support efficient operation. Fuel consumption includes, for example, fuel consumption data of the truck and consumption based on mileage, but is not limited to these examples. Driving time includes, for example, driving start time, rest time, driving end time, but is not limited to these examples. The monitoring unit, for example, monitors the truck's fuel consumption to support efficient operation. The monitoring unit can also monitor driving time to support efficient operation. The monitoring unit can also integrate fuel consumption and driving time to support efficient operation. In this way, monitoring the truck's fuel consumption and driving time enables efficient operation. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input fuel consumption and driving time data into AI and cause the AI ​​to perform support for efficient operation.

[0062] The monitoring unit can monitor the receipt status at the delivery destination and confirm receipt during monitoring. For example, the monitoring unit can monitor the receipt status at the delivery destination and confirm receipt during monitoring. The receipt status includes, for example, a timestamp of the receipt confirmation and a signature of the delivery, but is not limited to these examples. For example, the monitoring unit can monitor the receipt status at the delivery destination and confirm receipt. The monitoring unit can also use real-time data to monitor the receipt status. The monitoring unit can also confirm receipt and propose appropriate measures. This allows reliable delivery by monitoring the receipt status at the delivery destination and confirming receipt. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input data on the receipt status into AI and have the AI ​​execute the receipt confirmation.

[0063] The monitoring unit can monitor the maintenance status of the truck during monitoring and suggest necessary maintenance. For example, the monitoring unit can monitor the maintenance status of the truck during monitoring and suggest necessary maintenance. The maintenance status includes, but is not limited to, for example, a maintenance history and a next maintenance schedule. For example, the monitoring unit can monitor the maintenance status of the truck and suggest necessary maintenance. The monitoring unit can also use real-time data to monitor the maintenance status. The monitoring unit can also suggest necessary maintenance and support efficient operation. As a result, efficient operation is possible by monitoring the maintenance status of the truck and suggesting necessary maintenance. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input maintenance status data into AI and have the AI ​​execute the suggestion of necessary maintenance.

[0064] The monitoring unit can monitor weather information at the delivery destination during monitoring and adjust the delivery plan. For example, the monitoring unit can monitor weather information at the delivery destination during monitoring and adjust the delivery plan. Weather information includes, but is not limited to, data from the Japan Meteorological Agency and real-time weather information. For example, the monitoring unit can monitor weather information at the delivery destination and adjust the delivery plan. The monitoring unit can also use real-time data to monitor weather information. The monitoring unit can also propose an optimal delivery plan based on the weather information. This enables efficient delivery by monitoring weather information at the delivery destination and adjusting the delivery plan. Some or all of the above-mentioned processing by the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input weather information data into AI and have the AI ​​adjust the delivery plan.

[0065] The monitoring unit can monitor traffic conditions at the delivery destination during monitoring and assist in route recalculation. The monitoring unit, for example, monitors traffic conditions at the delivery destination during monitoring and assists in route recalculation. Traffic conditions include, but are not limited to, real-time traffic information and past traffic data. The monitoring unit, for example, monitors traffic conditions at the delivery destination and assists in route recalculation. The monitoring unit can also use real-time data to monitor traffic conditions. The monitoring unit can also recalculate an optimal route based on the traffic conditions. This enables efficient delivery by monitoring traffic conditions at the delivery destination and assisting in route recalculation. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input traffic condition data into AI and have the AI ​​recalculate the route.

[0066] The recalculation unit can recalculate the route by reflecting traffic congestion and accident information in real time during recalculation. For example, the recalculation unit recalculates the route by reflecting traffic congestion and accident information in real time during recalculation. Traffic congestion includes, for example, real-time traffic information and past traffic data, but is not limited to these examples. Accident information includes, for example, real-time traffic accident data and past accident data, but is not limited to these examples. For example, the recalculation unit reflects traffic congestion information in real time and recalculates the optimal route. The recalculation unit can also reflect accident information in real time and recalculate the optimal route. The recalculation unit can also integrate traffic congestion and accident information to recalculate the optimal route. In this way, the optimal route can be recalculated by reflecting traffic congestion and accident information in real time. Some or all of the above-described processing in the recalculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recalculation unit can input traffic congestion and accident information data into AI and cause the AI ​​to recalculate the optimal route.

[0067] The recalculation unit can recalculate the route by reflecting changes in the delivery destination's available pickup time during recalculation. For example, the recalculation unit recalculates the route by reflecting changes in the delivery destination's available pickup time during recalculation. Changes in available pickup time include, but are not limited to, changes in the customer's desired time slot and past pickup history. For example, the recalculation unit reflects changes in the delivery destination's available pickup time in real time and recalculates the optimal route. The recalculation unit can also compare multiple routes and select the optimal route, taking changes in available pickup time into consideration. The recalculation unit can also optimize the delivery order by reflecting changes in available pickup time. This enables efficient delivery by reflecting changes in the delivery destination's available pickup time. Some or all of the above-described processing by the recalculation unit may be performed using, or without, AI. For example, the recalculation unit can input data on changes in available pickup time into AI and have the AI ​​recalculate the optimal route.

[0068] The recalculation unit can propose an optimal refueling point by taking into account the truck's remaining fuel during recalculation. For example, the recalculation unit proposes an optimal refueling point by taking into account the truck's remaining fuel during recalculation. The remaining fuel may be, for example, a fuel gauge on the truck or a predicted remaining fuel amount based on the mileage, but is not limited to such examples. For example, the recalculation unit monitors the truck's remaining fuel in real time and proposes an optimal refueling point. The recalculation unit can also compare multiple refueling points and select the optimal point while taking into account the remaining fuel. The recalculation unit can also recalculate the optimal route by reflecting the remaining fuel. This enables efficient refueling by taking into account the truck's remaining fuel. Some or all of the above-described processing in the recalculation unit may be performed using, or without, AI. For example, the recalculation unit can input remaining fuel data into AI and have the AI ​​suggest the optimal refueling point.

[0069] The recalculation unit can recalculate the route taking into account weather information for the delivery destination when recalculating. For example, the recalculation unit recalculates the route taking into account weather information for the delivery destination when recalculating. Weather information includes, for example, data from the Japan Meteorological Agency and real-time weather information, but is not limited to these examples. For example, the recalculation unit reflects weather information for the delivery destination in real time and recalculates the optimal route. The recalculation unit can also compare multiple routes taking into account weather information and select the optimal route. The recalculation unit can also recalculate the optimal route taking into account weather information. This enables efficient delivery by taking into account weather information for the delivery destination. Some or all of the above-described processing by the recalculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recalculation unit can input weather information data into AI and have the AI ​​recalculate the optimal route.

[0070] The recalculation unit can recalculate the route taking into account traffic conditions at the delivery destination when recalculating. For example, the recalculation unit recalculates the route taking into account traffic conditions at the delivery destination when recalculating. Traffic conditions include, but are not limited to, real-time traffic information and past traffic data. For example, the recalculation unit reflects traffic conditions at the delivery destination in real time and recalculates the optimal route. The recalculation unit can also compare multiple routes taking into account traffic conditions and select the optimal route. The recalculation unit can also recalculate the optimal route taking into account traffic conditions. This enables efficient delivery by taking into account traffic conditions at the delivery destination. Some or all of the above-described processing in the recalculation unit may be performed using, or without, AI. For example, the recalculation unit can input traffic condition data into AI and have the AI ​​recalculate the optimal route.

[0071] The recalculation unit can recalculate the route taking into account the truck's maintenance status during recalculation. For example, the recalculation unit recalculates the route taking into account the truck's maintenance status during recalculation. The maintenance status includes, for example, a maintenance history and a next maintenance schedule, but is not limited to these examples. For example, the recalculation unit reflects the truck's maintenance status in real time and recalculates the optimal route. The recalculation unit can also compare multiple routes taking into account the maintenance status and select the optimal route. The recalculation unit can also recalculate the optimal route taking into account the maintenance status. This enables efficient operation by taking into account the truck's maintenance status. Some or all of the above-described processing in the recalculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recalculation unit can input maintenance status data into AI and have the AI ​​recalculate the optimal route.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The collection unit can collect social media trend data in addition to past sales data and seasonal demand fluctuations. For example, the collection unit analyzes hashtag frequency and post content and reflects the results in demand forecasts. The collection unit can also analyze social media post content to predict demand fluctuations. Furthermore, the collection unit can integrate social media trend data with other data to make more accurate demand forecasts. In this way, collecting social media trend data improves the accuracy of demand forecasts.

[0074] The prediction unit can improve prediction accuracy by combining different prediction algorithms during prediction. For example, the prediction unit can improve prediction accuracy by combining time series analysis and a machine learning model. The prediction unit can also compare multiple prediction algorithms and select the algorithm with the highest accuracy. Furthermore, the prediction unit can improve the reliability of the prediction results by combining different prediction algorithms. As a result, prediction accuracy is improved by combining different prediction algorithms.

[0075] The route calculation unit can optimize the route by taking into account the risk of traffic accidents and natural disasters when calculating the route. For example, the route calculation unit can propose the optimal route by taking into account real-time data on traffic accidents and past accident data. The route calculation unit can also propose the optimal route by taking into account the risk of natural disasters. Furthermore, the route calculation unit can also propose the optimal route by integrating the risk of traffic accidents and natural disasters. This makes it possible to propose a safer route by taking into account the risk of traffic accidents and natural disasters.

[0076] The inventory allocation unit can optimize inventory allocation by taking into account product storage conditions when allocating inventory. For example, the inventory allocation unit proposes optimal inventory allocation by taking into account product temperature, humidity, and light conditions. The inventory allocation unit can also optimize product allocation by taking into account temperature and humidity. Furthermore, the inventory allocation unit can integrate storage conditions and propose optimal inventory allocation. This makes it possible to optimize inventory allocation by taking into account product storage conditions.

[0077] During monitoring, the monitoring unit can monitor the truck's fuel consumption and driving time to support efficient operation. For example, the monitoring unit can monitor the truck's fuel consumption data and consumption based on the mileage to support efficient operation. The monitoring unit can also monitor the driving start time, rest time, and driving end time to support efficient operation. Furthermore, the monitoring unit can integrate the fuel consumption and driving time to support efficient operation. In this way, efficient operation is possible by monitoring the truck's fuel consumption and driving time.

[0078] The processing flow of the first embodiment will be briefly explained below.

[0079] Step 1: The collection unit collects demand data. Demand data includes sales data, customer behavior data, and market trend data. The collection unit collects past sales data, seasonal demand fluctuation data, daily sales data, and monthly sales data. Step 2: The forecasting unit performs demand forecasting based on the data collected by the collection unit. The forecasting unit analyzes past data using time series analysis, regression analysis, and machine learning models to predict future demand. Step 3: The route calculation unit calculates the optimal logistics route based on the demand predicted by the forecasting unit. The route calculation unit takes into account traffic conditions and inventory placement, and calculates the route based on minimizing distance, time, and cost. Step 4: The inventory allocation unit optimizes inventory allocation based on the route calculated by the route calculation unit. The inventory allocation unit allocates more inventory in areas with high demand and less inventory in areas with low demand. Step 5: The monitoring unit monitors the progress of the inventory allocated by the inventory allocation unit in real time. The monitoring unit obtains the location information of the truck using GPS and grasps the progress of logistics. Step 6: The recalculation unit recalculates routes and reallocates inventory based on the progress monitored by the monitoring unit. If a traffic jam occurs, the recalculation unit calculates a new route and issues instructions to the truck. The route is recalculated according to changes in traffic conditions.

[0080] (Example 2) A logistics optimization system according to an embodiment of the present invention collects demand data, forecasts demand using AI, calculates optimal logistics routes, and optimizes inventory allocation. The logistics optimization system collects demand data, forecasts demand using AI, calculates optimal logistics routes, and optimizes inventory allocation. Furthermore, the logistics optimization system monitors the progress of logistics in real time and recalculates routes and reallocates inventory as necessary. For example, the logistics optimization system collects demand data for each region, including past sales data and seasonal demand fluctuations. The logistics optimization system then inputs the collected data into AI to perform a demand forecast. The AI ​​analyzes the past data and predicts future demand. The logistics optimization system then calculates optimal logistics routes based on the predicted demand. The AI ​​then proposes optimal routes taking into account traffic conditions and inventory allocation. The logistics optimization system then optimizes inventory allocation. For example, the system allocates more inventory in areas with high demand and less inventory in areas with low demand. The logistics optimization system then monitors the progress of logistics in real time. For example, the system uses GPS to obtain truck location information and grasps the progress of logistics. Next, the logistics optimization system recalculates routes and reallocates inventory as necessary. For example, if a traffic jam occurs, the AI ​​calculates a new route and issues instructions to trucks. In this way, the logistics optimization system can reduce logistics lead times to as close to zero as possible. This allows the logistics optimization system to significantly shorten the time it takes for products to reach customers. For example, customers can receive products the moment they want them, improving satisfaction. Companies can also expect to see reductions in inventory management and logistics costs.

[0081] A logistics optimization system according to an embodiment includes a collection unit, a prediction unit, a route calculation unit, an inventory allocation unit, a monitoring unit, and a recalculation unit. The collection unit collects demand data. Demand data includes, but is not limited to, sales data, customer behavior data, and market trend data. The collection unit collects, for example, past sales data. The collection unit can also collect seasonal demand fluctuation data. For example, the collection unit collects daily sales data and monthly sales data. The prediction unit performs demand forecasting based on the data collected by the collection unit. The prediction unit performs demand forecasting using, for example, time series analysis, regression analysis, or a machine learning model. For example, the prediction unit analyzes past data and predicts future demand. The route calculation unit calculates an optimal logistics route based on the demand predicted by the prediction unit. The route calculation unit calculates the optimal route taking into account, for example, traffic conditions and inventory allocation status. For example, the route calculation unit calculates a route based on minimizing distance, time, and cost. The inventory allocation unit optimizes inventory allocation based on the route calculated by the route calculation unit. For example, the inventory allocation unit allocates more inventory in areas with high demand and less inventory in areas with low demand. For example, the inventory allocation unit allocates inventory based on a demand forecast. The monitoring unit monitors the progress of the inventory allocated by the inventory allocation unit in real time. For example, the monitoring unit obtains truck location information using GPS and grasps the progress of logistics. For example, the monitoring unit monitors the progress based on the real-time location information. The recalculation unit recalculates routes and relocates inventory based on the progress monitored by the monitoring unit. For example, the recalculation unit calculates a new route when traffic congestion occurs and issues instructions to the truck. For example, the recalculation unit recalculates routes in response to changes in traffic conditions. As a result, the logistics optimization system according to the embodiment can reduce logistics lead times to as close to zero as possible by consistently performing processes from demand data collection to forecasting, route calculation, inventory allocation, progress monitoring, and recalculation.

[0082] The collection unit can collect past sales data or data on seasonal demand fluctuations. The collection unit, for example, collects past sales data. Past sales data includes, but is not limited to, daily sales data, monthly sales data, and product-specific sales data. The collection unit can also collect seasonal demand fluctuation data. For example, the collection unit collects seasonal sales data and the effects of seasonal events. This enables more accurate demand forecasting by taking past sales data and seasonal demand fluctuations into account. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past sales data into AI and have the AI ​​collect the data.

[0083] The prediction unit can analyze the collected data and predict future demand. The prediction unit, for example, analyzes the collected data and predicts future demand. The collected data includes, but is not limited to, sales data, customer behavior data, and market trend data. The prediction unit performs demand prediction using, for example, time series analysis, regression analysis, or a machine learning model. For example, the prediction unit analyzes past data and predicts future demand. In this way, future demand can be accurately predicted by analyzing the collected data. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the collected data into AI and have the AI ​​perform demand prediction.

[0084] The route calculation unit can calculate an optimal logistics route by taking into account traffic conditions and inventory distribution conditions. The route calculation unit calculates the optimal logistics route by taking into account, for example, traffic conditions and inventory distribution conditions. Examples of traffic conditions include, but are not limited to, real-time traffic information and past traffic data. Examples of inventory distribution conditions include, but are not limited to, the amount of inventory in each warehouse and the location of products. The route calculation unit calculates a route based on, for example, minimizing distance, minimizing time, and optimizing cost. For example, the route calculation unit proposes an optimal route by taking into account traffic conditions and inventory distribution conditions. This makes it possible to calculate an optimal logistics route by taking into account traffic conditions and inventory distribution conditions. Some or all of the above-described processing in the route calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the route calculation unit may input data on traffic conditions and inventory distribution conditions into AI and have the AI ​​calculate the optimal route.

[0085] The inventory allocation unit can allocate more inventory in areas with high demand and less inventory in areas with low demand. For example, the inventory allocation unit allocates more inventory in areas with high demand and less inventory in areas with low demand. Areas with high demand include, but are not limited to, past sales data and demand forecasts for each area. Areas with low demand include, but are not limited to, past sales data and demand forecasts for each area. This enables inventory optimization by allocating inventory according to demand. Some or all of the above-described processing in the inventory allocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the inventory allocation unit may input demand forecast data into AI and have the AI ​​execute inventory allocation optimization.

[0086] The monitoring unit can obtain truck location information using GPS and grasp the progress of logistics. The monitoring unit, for example, obtains truck location information using GPS and grasps the progress of logistics. The progress of logistics includes, for example, real-time location information and inventory consumption status, but is not limited to these examples. The monitoring unit, for example, monitors the progress based on real-time location information. In this way, by using GPS, the location information of the truck can be grasped in real time. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input GPS data into AI and have the AI ​​monitor the progress.

[0087] The recalculation unit can calculate a new route when a traffic jam occurs and issue instructions to the truck. For example, the recalculation unit calculates a new route when a traffic jam occurs and issues instructions to the truck. Traffic jams include, but are not limited to, real-time traffic information and past traffic data. For example, the recalculation unit recalculates the route in accordance with changes in traffic conditions. This makes it possible to quickly calculate a new route and issue instructions to the truck even when a traffic jam occurs. Some or all of the above-mentioned processing in the recalculation unit may be performed using, or without, an AI. For example, the recalculation unit can input traffic jam data into an AI and have the AI ​​calculate a new route.

[0088] The collection unit can estimate a user's emotion and adjust the timing of demand data collection based on the estimated user emotion. The collection unit, for example, estimates a user's emotion and adjusts the timing of demand data collection based on the estimated user emotion. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. For example, when the user is feeling stressed, the collection unit delays the collection timing to reduce the user's burden. Furthermore, when the user is relaxed, the collection unit can also advance the collection timing to quickly collect data. Furthermore, when the user is in a hurry, the collection unit can optimize the collection timing to efficiently collect data. This enables efficient data collection by adjusting the collection timing according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0089] The collection unit can collect weather data and event information in addition to past sales data and seasonal demand fluctuations. For example, the collection unit collects weather data and event information in addition to past sales data and seasonal demand fluctuations. Weather data includes, but is not limited to, data from the Japan Meteorological Agency and real-time weather information. Event information includes, but is not limited to, a local event calendar and past event data. For example, the collection unit collects weather data in addition to past sales data and reflects it in a demand forecast. The collection unit can also collect local event information in addition to seasonal demand fluctuations and use it in a demand forecast. The collection unit can also integrate past sales data, weather data, and event information to perform more accurate demand forecasts. Collecting weather data and event information thereby enables more accurate demand forecasts. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input weather data and event information into AI and have the AI ​​collect the data.

[0090] The collection unit can evaluate the reliability of data at the time of collection and exclude unreliable data. For example, the collection unit evaluates the reliability of data at the time of collection and excludes unreliable data. Data reliability includes, but is not limited to, the reliability of the data source and the consistency of the data. For example, the collection unit uses AI to evaluate the reliability of the collected data and automatically exclude unreliable data. The collection unit can also cross-check multiple data sources to evaluate the reliability of the data. The collection unit can also evaluate the source of the data and the collection method to exclude unreliable data. In this way, by evaluating the reliability of the data, unreliable data can be excluded and highly accurate predictions can be made. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can have AI perform the data reliability evaluation.

[0091] The collection unit can detect duplicate data during collection and integrate the duplicate data. For example, the collection unit can detect duplicate data during collection and integrate the duplicate data. Examples of data duplication include, but are not limited to, a database duplication check algorithm. For example, the collection unit can use AI to detect duplicate data and automatically integrate the collected data. The collection unit can also evaluate the uniqueness of the data to detect duplicate data. The collection unit can also compare the contents of the data and select the most reliable data to integrate the duplicate data. This allows the detection and integration of duplicate data to maintain data consistency. Some or all of the above-described processing in the collection unit can be performed using AI, or can be performed without AI. For example, the collection unit can have AI perform the detection and integration of duplicate data.

[0092] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of data to be collected based on the estimated user emotions. User emotions include, but are not limited to, stress, relaxation, and hurry. For example, when the user is feeling stressed, the collection unit prioritizes collecting data of high importance. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed data. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting data that can be collected quickly. This enables efficient data collection by determining the priority of data according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0093] The collection unit can collect social media trend data during collection and use it for demand forecasting. For example, the collection unit collects social media trend data during collection and uses it for demand forecasting. Social media trend data includes, but is not limited to, hashtag frequency analysis and post content analysis. For example, the collection unit collects social media trend data and reflects it in demand forecasting. The collection unit can also analyze social media post content to predict demand fluctuations. The collection unit can also integrate social media trend data with other data to perform more accurate demand forecasting. In this way, collecting social media trend data improves the accuracy of demand forecasting. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media trend data into AI and have the AI ​​collect data.

[0094] The collection unit may collect competitors' sales data and perform comparative analysis during collection. For example, the collection unit may collect competitors' sales data and perform comparative analysis during collection. Examples of competitors' sales data include, but are not limited to, market research reports and publicly available sales data. For example, the collection unit may collect competitors' sales data and compare it with the company's own sales data. The collection unit may also analyze competitors' sales data and predict demand fluctuations. The collection unit may also integrate competitors' sales data with other data to perform more accurate demand forecasts. This enables more accurate demand forecasts by collecting competitors' sales data and performing comparative analysis. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input competitors' sales data into AI and have the AI ​​perform data collection and analysis.

[0095] The collection unit can collect regional economic indicator data at the time of collection and reflect it in the demand forecast. For example, the collection unit collects regional economic indicator data at the time of collection and reflects it in the demand forecast. Regional economic indicator data includes, but is not limited to, government statistical data and economic reports. For example, the collection unit collects regional economic indicator data and reflects it in the demand forecast. The collection unit can also analyze the economic indicator data and predict demand fluctuations. The collection unit can also integrate the economic indicator data with other data to perform more accurate demand forecasts. In this way, collecting regional economic indicator data improves the accuracy of the demand forecast. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input regional economic indicator data into AI and have the AI ​​collect and analyze the data.

[0096] The prediction unit can estimate the user's emotion and adjust the way in which the prediction result is expressed based on the estimated user's emotion. The prediction unit, for example, estimates the user's emotion and adjusts the way in which the prediction result is expressed based on the estimated user's emotion. User emotions include, but are not limited to, stress, relaxation, and hurry. For example, if the user is nervous, the prediction unit can provide a simple, highly visible prediction result. If the user is relaxed, the prediction unit can also provide a prediction result that includes detailed information. If the user is hurrying, the prediction unit can also provide a prediction result that focuses on the main points. This allows the prediction result to be more easily understood by adjusting the way in which the prediction result is expressed based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0097] The prediction unit can improve prediction accuracy by combining different prediction algorithms during prediction. The prediction unit, for example, improves prediction accuracy by combining different prediction algorithms during prediction. Different prediction algorithms include, but are not limited to, a combination of time series analysis and a machine learning model. The prediction unit, for example, combines different prediction algorithms to improve prediction accuracy. The prediction unit can also compare multiple prediction algorithms and select the algorithm with the highest accuracy. The prediction unit can also combine different prediction algorithms to improve the reliability of the prediction results. As a result, combining different prediction algorithms improves prediction accuracy. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input different prediction algorithms into AI and have the AI ​​improve prediction accuracy.

[0098] The prediction unit can compare past prediction results with actual demand during prediction and improve the prediction model. For example, the prediction unit compares past prediction results with actual demand during prediction and improves the prediction model. Past prediction results include, but are not limited to, past sales prediction data and actual sales data. For example, the prediction unit compares past prediction results with actual demand and improves the prediction model. The prediction unit can also analyze errors in the prediction results and improve the accuracy of the prediction model. The prediction unit can also improve the reliability of the prediction model by comparing past prediction results with actual demand. Comparing past prediction results with actual demand improves the accuracy of the prediction model. Some or all of the above-described processing in the prediction unit can be performed using, for example, AI, or without AI. For example, the prediction unit can input past prediction results and actual demand data into AI and have the AI ​​improve the prediction model.

[0099] The prediction unit can detect a sudden change in demand during prediction and issue an alert. For example, the prediction unit detects a sudden change in demand during prediction and issues an alert. Examples of sudden changes in demand include, but are not limited to, an anomaly detection algorithm and threshold setting. For example, the prediction unit uses AI to detect a sudden change in demand and issue an alert. The prediction unit can also monitor real-time data to detect a sudden change in demand. The prediction unit can also detect a sudden change in demand and propose an appropriate response. This enables a rapid response by detecting a sudden change in demand and issuing an alert. Some or all of the above-mentioned processing in the prediction unit may be performed using AI, for example, or may be performed without using AI. For example, the prediction unit can input data on sudden changes in demand into AI and have the AI ​​detect the change and issue an alert.

[0100] The prediction unit can estimate the user's emotion and adjust the level of detail of the prediction result based on the estimated user's emotion. The prediction unit, for example, estimates the user's emotion and adjusts the level of detail of the prediction result based on the estimated user's emotion. User emotions include, but are not limited to, stress, relaxation, and hurry. For example, if the user is nervous, the prediction unit can provide a simple, highly visible prediction result. If the user is relaxed, the prediction unit can also provide a prediction result that includes detailed information. If the user is hurrying, the prediction unit can also provide a prediction result that focuses on the main points. This allows for more appropriate information to be provided by adjusting the level of detail of the prediction result according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0101] The prediction unit can perform demand prediction on different time axes during prediction. The prediction unit, for example, performs demand prediction on different time axes during prediction. Different time axes include, but are not limited to, short-term (one week), medium-term (one month), and long-term (one year) periods. The prediction unit, for example, performs short-term demand prediction and proposes immediate responses. The prediction unit can also perform medium-term demand prediction and propose planned responses. The prediction unit can also perform long-term demand prediction and propose strategic responses. In this way, by performing demand prediction on different time axes, planning from short-term to long-term becomes possible. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input data on different time axes into AI and have the AI ​​perform demand prediction.

[0102] The forecasting unit can perform demand forecasts for different regions and reflect regional characteristics during forecasting. For example, the forecasting unit performs demand forecasts for different regions and reflects regional characteristics during forecasting. Examples of different regions include, but are not limited to, urban areas, suburban areas, and rural areas. For example, the forecasting unit can perform demand forecasts for different regions and reflect regional characteristics. Furthermore, the forecasting unit can also propose optimal inventory allocations based on the demand forecasts for each region. Furthermore, the forecasting unit can perform demand forecasts that take regional characteristics into account, thereby improving accuracy. By performing demand forecasts that reflect regional characteristics, optimal responses for each region are possible. Some or all of the above-described processing in the forecasting unit may be performed using, for example, AI, or may be performed without using AI. For example, the forecasting unit can input data for each region into AI and have the AI ​​perform demand forecasts.

[0103] The prediction unit can perform demand forecasts for different product categories during prediction. For example, the prediction unit performs demand forecasts for different product categories during prediction. Different product categories include, but are not limited to, food, clothing, and electronic devices. For example, the prediction unit performs demand forecasts for different product categories and reflects category characteristics. The prediction unit can also propose optimal inventory allocation based on the demand forecast for each product category. The prediction unit can also perform demand forecasts that take product category characteristics into account to improve accuracy. As a result, performing demand forecasts for each product category enables responses that reflect category characteristics. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input data for each product category into AI and have the AI ​​perform demand forecasts.

[0104] The route calculation unit can estimate the user's emotions and adjust the display method of the route calculation results based on the estimated user emotions. The route calculation unit, for example, estimates the user's emotions and adjusts the display method of the route calculation results based on the estimated user emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. For example, if the user is nervous, the route calculation unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the route calculation unit can provide a display method that includes detailed information. Furthermore, if the user is hurrying, the route calculation unit can provide a display method that focuses on the main points. This allows for adjusting the display method of the route calculation results according to the user's emotions, thereby providing more easily understandable information. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the route calculation unit may be performed using, for example, an AI, or without an AI. For example, the route calculation unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0105] The route calculation unit can optimize the route by taking into account the risk of traffic accidents and natural disasters when calculating the route. The route calculation unit, for example, optimizes the route by taking into account the risk of traffic accidents and natural disasters when calculating the route. Traffic accidents include, for example, real-time traffic accident data and past accident data, but are not limited to these examples. Risks of natural disasters include, for example, weather data and earthquake data, but are not limited to these examples. The route calculation unit, for example, proposes an optimal route by taking into account the risk of traffic accidents. The route calculation unit can also propose an optimal route by taking into account the risk of natural disasters. The route calculation unit can also propose an optimal route by integrating the risks of traffic accidents and natural disasters. This makes it possible to propose a safer route by taking into account the risk of traffic accidents and natural disasters. Some or all of the above-described processing in the route calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the route calculation unit can input risk data of traffic accidents and natural disasters into AI and have the AI ​​calculate the optimal route.

[0106] The route calculation unit can propose an optimal route by combining multiple delivery means when calculating a route. For example, the route calculation unit proposes an optimal route by combining multiple delivery means (truck, rail, ship, etc.) when calculating a route. Examples of multiple delivery means include, but are not limited to, truck, rail, ship, and air. For example, the route calculation unit proposes an optimal route by combining truck and rail. The route calculation unit can also propose an optimal route by combining truck and ship. The route calculation unit can also propose an optimal route by combining multiple delivery means. In this way, an optimal route can be proposed by combining multiple delivery means. Some or all of the above-described processing in the route calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the route calculation unit can input data on multiple delivery means into AI and have the AI ​​calculate the optimal route.

[0107] The route calculation unit may calculate a route that minimizes energy consumption during route calculation. For example, the route calculation unit may calculate a route that minimizes energy consumption during route calculation. Energy consumption may include, but is not limited to, fuel consumption and power consumption. For example, the route calculation unit may propose an optimal route to minimize energy consumption. The route calculation unit may also compare multiple routes and select the optimal route, taking energy consumption into consideration. The route calculation unit may also consider real-time traffic information to minimize energy consumption. By minimizing energy consumption, this reduces environmental impact and costs. Some or all of the above-described processing in the route calculation unit may be performed using, or without, AI. For example, the route calculation unit may input energy consumption data into AI and have the AI ​​calculate the optimal route.

[0108] The route calculation unit can estimate the user's emotions and determine the priority of route calculation based on the estimated user emotions. The route calculation unit, for example, estimates the user's emotions and determines the priority of route calculation based on the estimated user emotions. User emotions include, but are not limited to, stress, relaxation, and hurry. For example, when the user is stressed, the route calculation unit quickly calculates the route and proposes the optimal route. Furthermore, when the user is relaxed, the route calculation unit can calculate a detailed route and propose multiple routes. Furthermore, when the user is in a hurry, the route calculation unit can prioritize the route that can be reached in the shortest time. This enables efficient route calculation by determining the priority of route calculation based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the route calculation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the route calculation unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0109] The route calculation unit can optimize the route by taking into account the available pickup times at the delivery destinations when calculating the route. For example, the route calculation unit optimizes the route by taking into account the available pickup times at the delivery destinations when calculating the route. The available pickup times at the delivery destinations include, but are not limited to, the customer's desired time slot and past pickup history. The route calculation unit, for example, proposes an optimal route by taking into account the available pickup times at the delivery destinations. The route calculation unit can also calculate a route that efficiently travels around multiple delivery destinations with different available pickup times. The route calculation unit can also optimize the delivery order by taking into account the available pickup times. This enables efficient delivery by taking into account the available pickup times at the delivery destinations. Some or all of the above-described processing in the route calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the route calculation unit can input data on the available pickup times into AI and have the AI ​​calculate the optimal route.

[0110] The route calculation unit can optimize the route by taking into account the geographical conditions of the delivery destination when calculating the route. For example, the route calculation unit optimizes the route by taking into account the geographical conditions of the delivery destination (mountainous area, urban area, etc.) when calculating the route. Geographical conditions of the delivery destination include, but are not limited to, mountainous area, urban area, and suburban area. For example, the route calculation unit proposes an optimal route by taking into account a delivery destination in a mountainous area. The route calculation unit can also propose an optimal route by taking into account a delivery destination in an urban area. The route calculation unit can also compare multiple routes by taking into account the geographical conditions and select the optimal route. In this way, the optimal route can be proposed by taking the geographical conditions into account. Some or all of the above-described processing in the route calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the route calculation unit can input data on geographical conditions into AI and have the AI ​​calculate the optimal route.

[0111] The route calculation unit can optimize the route by taking into account specific requirements of the delivery destination when calculating the route. For example, the route calculation unit optimizes the route by taking into account specific requirements (e.g., refrigeration, freezing) of the delivery destination when calculating the route. Specific requirements of the delivery destination include, but are not limited to, refrigeration, freezing, and specific temperature control. For example, the route calculation unit proposes an optimal route by taking into account the delivery destination of refrigerated goods. The route calculation unit can also propose an optimal route by taking into account the delivery destination of frozen goods. The route calculation unit can also compare multiple routes by taking into account the specific requirements and select the optimal route. In this way, the optimal route can be proposed by taking the specific requirements into account. Some or all of the above-described processing in the route calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the route calculation unit can input data of the specific requirements into AI and have the AI ​​calculate the optimal route.

[0112] The inventory allocation unit can estimate a user's emotions and adjust the inventory allocation method based on the estimated user emotions. For example, the inventory allocation unit can estimate a user's emotions and adjust the inventory allocation method based on the estimated user emotions. User emotions include, but are not limited to, stress, relaxation, and hurry. For example, the inventory allocation unit can provide a simple inventory allocation method when the user is stressed. Furthermore, the inventory allocation unit can provide a detailed inventory allocation method when the user is relaxed. Furthermore, the inventory allocation unit can provide a quick inventory allocation method when the user is hurrying. This enables efficient inventory management by adjusting the inventory allocation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the inventory allocation unit can be performed using, for example, an AI, or without an AI. For example, the inventory allocation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0113] The inventory allocation unit can optimize inventory allocation by taking into account the warehouse capacity and available space. For example, the inventory allocation unit optimizes inventory allocation by taking into account the warehouse capacity and available space. Examples of warehouse capacity include, but are not limited to, the total capacity of the warehouse and the currently used capacity. Examples of available space include, but are not limited to, available shelf space and available pallet space. For example, the inventory allocation unit proposes optimal inventory allocation by taking into account the warehouse capacity. The inventory allocation unit can also optimize inventory allocation by taking into account the available space. The inventory allocation unit can also integrate the warehouse capacity and the available space to propose optimal inventory allocation. This enables efficient inventory allocation by taking into account the warehouse capacity and available space. Some or all of the above-described processing in the inventory allocation unit may be performed using, or without, AI. For example, the inventory allocation unit can input data on the warehouse capacity and available space into AI and have the AI ​​calculate the optimal inventory allocation.

[0114] The inventory allocation unit can optimize inventory allocation by taking into account product storage conditions during inventory allocation. For example, the inventory allocation unit optimizes inventory allocation by taking into account product storage conditions (temperature, humidity, etc.) during inventory allocation. Product storage conditions include, but are not limited to, temperature, humidity, and light conditions. For example, the inventory allocation unit proposes an optimal inventory allocation by taking into account product storage conditions. The inventory allocation unit can also optimize product allocation by taking into account temperature and humidity. The inventory allocation unit can also integrate storage conditions and propose an optimal inventory allocation. This enables optimal inventory allocation by taking into account product storage conditions. Some or all of the above-described processing in the inventory allocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the inventory allocation unit can input data on product storage conditions into AI and have the AI ​​calculate the optimal inventory allocation.

[0115] The inventory allocation unit can optimize inventory allocation by taking product turnover into consideration when allocating inventory. For example, the inventory allocation unit optimizes inventory allocation by taking product turnover into consideration when allocating inventory. Product turnover includes, but is not limited to, monthly sales volume and inventory retention period. For example, the inventory allocation unit proposes optimal inventory allocation by taking product turnover into consideration. The inventory allocation unit can also prioritize the allocation of products with high turnover rates. The inventory allocation unit can also integrate turnover rates to propose optimal inventory allocation. This enables efficient inventory management by taking product turnover into consideration. Some or all of the above-described processing in the inventory allocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the inventory allocation unit can input product turnover data into AI and have the AI ​​calculate the optimal inventory allocation.

[0116] The inventory allocation unit can estimate a user's emotions and determine inventory allocation priorities based on the estimated user emotions. The inventory allocation unit, for example, estimates a user's emotions and determines inventory allocation priorities based on the estimated user emotions. User emotions include, but are not limited to, stress, relaxation, and hurry. For example, when a user is feeling stressed, the inventory allocation unit prioritizes allocation of important inventory. Furthermore, when a user is relaxed, the inventory allocation unit can also perform detailed inventory allocation. Furthermore, when a user is in a hurry, the inventory allocation unit can also perform quick inventory allocation. This enables efficient inventory management by determining inventory allocation priorities based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the inventory allocation unit may be performed using, for example, an AI, or without an AI. For example, the inventory allocation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0117] The inventory allocation unit can optimize inventory allocation by taking into account the size and weight of the products when allocating inventory. For example, the inventory allocation unit optimizes inventory allocation by taking into account the size and weight of the products when allocating inventory. Product size includes, but is not limited to, product dimensions and package size. Product weight includes, but is not limited to, the total weight of the products and package weight. For example, the inventory allocation unit proposes optimal inventory allocation by taking into account the size of the products. The inventory allocation unit can also optimize inventory allocation by taking into account the weight of the products. The inventory allocation unit can also integrate size and weight to propose optimal inventory allocation. This enables efficient inventory management by taking into account the size and weight of the products. Some or all of the above-described processing in the inventory allocation unit may be performed using, or without, AI. For example, the inventory allocation unit can input data on the size and weight of products into AI and have the AI ​​calculate the optimal inventory allocation.

[0118] The inventory allocation unit can optimize inventory allocation by taking into account the shipping frequency of the product when allocating inventory. For example, the inventory allocation unit optimizes inventory allocation by taking into account the shipping frequency of the product when allocating inventory. Product shipping frequency includes, but is not limited to, daily shipping volume and monthly shipping volume. For example, the inventory allocation unit proposes an optimal inventory allocation by taking into account the shipping frequency of the product. The inventory allocation unit can also prioritize the allocation of products with high shipping frequencies. The inventory allocation unit can also integrate shipping frequencies to propose an optimal inventory allocation. This enables efficient inventory management by taking into account the shipping frequency of the product. Some or all of the above-described processing in the inventory allocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the inventory allocation unit can input data on the shipping frequency of the product into AI and have the AI ​​calculate the optimal inventory allocation.

[0119] The inventory allocation unit can optimize inventory allocation by taking into account product storage costs when allocating inventory. For example, the inventory allocation unit optimizes inventory allocation by taking into account product storage costs when allocating inventory. Product storage costs include, but are not limited to, warehouse rent and costs according to storage period. For example, the inventory allocation unit proposes optimal inventory allocation by taking into account product storage costs. The inventory allocation unit can also prioritize product allocation with low storage costs. The inventory allocation unit can also integrate storage costs to propose optimal inventory allocation. This enables efficient inventory management by taking product storage costs into account. Some or all of the above-described processing in the inventory allocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the inventory allocation unit can input product storage cost data into AI and have the AI ​​calculate the optimal inventory allocation.

[0120] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. The monitoring unit, for example, estimates the user's emotions and adjusts the display method of the monitoring results based on the estimated user emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. For example, if the user is nervous, the monitoring unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the monitoring unit can provide a display method that includes detailed information. Furthermore, if the user is hurrying, the monitoring unit can provide a display method that focuses on the main points. This allows for adjusting the display method of the monitoring results according to the user's emotions, thereby providing more understandable information. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the monitoring unit may be performed using, for example, an AI, or without an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0121] The monitoring unit can visualize the progress of logistics in real time and detect abnormalities during monitoring. For example, the monitoring unit visualizes the progress of logistics in real time and detects abnormalities during monitoring. Examples of the progress of logistics include, but are not limited to, real-time location information and inventory consumption status. Examples of abnormalities include, but are not limited to, delays from scheduled times and inventory shortages. For example, the monitoring unit visualizes the progress of logistics in real time and detects abnormalities. The monitoring unit can also monitor real-time data to detect abnormalities. The monitoring unit can also detect abnormalities and propose appropriate responses. This enables rapid responses by visualizing the progress of logistics in real time and detecting abnormalities. Some or all of the above-described processing by the monitoring unit may be performed using, for example, AI, or without AI. For example, the monitoring unit can input logistics progress data into AI and have the AI ​​detect abnormalities.

[0122] The monitoring unit can monitor the truck's fuel consumption and driving time during monitoring to support efficient operation. The monitoring unit, for example, monitors the truck's fuel consumption and driving time during monitoring to support efficient operation. Fuel consumption includes, for example, fuel consumption data of the truck and consumption based on mileage, but is not limited to these examples. Driving time includes, for example, driving start time, rest time, driving end time, but is not limited to these examples. The monitoring unit, for example, monitors the truck's fuel consumption to support efficient operation. The monitoring unit can also monitor driving time to support efficient operation. The monitoring unit can also integrate fuel consumption and driving time to support efficient operation. In this way, monitoring the truck's fuel consumption and driving time enables efficient operation. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input fuel consumption and driving time data into AI and cause the AI ​​to perform support for efficient operation.

[0123] The monitoring unit can monitor the receipt status at the delivery destination and confirm receipt during monitoring. For example, the monitoring unit can monitor the receipt status at the delivery destination and confirm receipt during monitoring. The receipt status includes, for example, a timestamp of the receipt confirmation and a signature of the delivery, but is not limited to these examples. For example, the monitoring unit can monitor the receipt status at the delivery destination and confirm receipt. The monitoring unit can also use real-time data to monitor the receipt status. The monitoring unit can also confirm receipt and propose appropriate measures. This allows reliable delivery by monitoring the receipt status at the delivery destination and confirming receipt. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input data on the receipt status into AI and have the AI ​​execute the receipt confirmation.

[0124] The monitoring unit can estimate the user's emotions and prioritize the monitoring results based on the estimated user emotions. The monitoring unit, for example, estimates the user's emotions and prioritizes the monitoring results based on the estimated user emotions. User emotions include, but are not limited to, stress, relaxation, and hurry. For example, when the user is feeling stressed, the monitoring unit can prioritize and display monitoring results with high importance. Furthermore, when the user is relaxed, the monitoring unit can display detailed monitoring results. Furthermore, when the user is in a hurry, the monitoring unit can prioritize and display monitoring results that require a prompt response. This enables efficient monitoring by prioritizing the monitoring results according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0125] The monitoring unit can monitor the maintenance status of the truck during monitoring and suggest necessary maintenance. For example, the monitoring unit can monitor the maintenance status of the truck during monitoring and suggest necessary maintenance. The maintenance status includes, but is not limited to, for example, a maintenance history and a next maintenance schedule. For example, the monitoring unit can monitor the maintenance status of the truck and suggest necessary maintenance. The monitoring unit can also use real-time data to monitor the maintenance status. The monitoring unit can also suggest necessary maintenance and support efficient operation. As a result, efficient operation is possible by monitoring the maintenance status of the truck and suggesting necessary maintenance. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input maintenance status data into AI and have the AI ​​execute the suggestion of necessary maintenance.

[0126] The monitoring unit can monitor weather information at the delivery destination during monitoring and adjust the delivery plan. For example, the monitoring unit can monitor weather information at the delivery destination during monitoring and adjust the delivery plan. Weather information includes, but is not limited to, data from the Japan Meteorological Agency and real-time weather information. For example, the monitoring unit can monitor weather information at the delivery destination and adjust the delivery plan. The monitoring unit can also use real-time data to monitor weather information. The monitoring unit can also propose an optimal delivery plan based on the weather information. This enables efficient delivery by monitoring weather information at the delivery destination and adjusting the delivery plan. Some or all of the above-mentioned processing by the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input weather information data into AI and have the AI ​​adjust the delivery plan.

[0127] The monitoring unit can monitor traffic conditions at the delivery destination during monitoring and assist in route recalculation. The monitoring unit, for example, monitors traffic conditions at the delivery destination during monitoring and assists in route recalculation. Traffic conditions include, but are not limited to, real-time traffic information and past traffic data. The monitoring unit, for example, monitors traffic conditions at the delivery destination and assists in route recalculation. The monitoring unit can also use real-time data to monitor traffic conditions. The monitoring unit can also recalculate an optimal route based on the traffic conditions. This enables efficient delivery by monitoring traffic conditions at the delivery destination and assisting in route recalculation. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input traffic condition data into AI and have the AI ​​recalculate the route.

[0128] The recalculation unit can estimate the user's emotion and adjust the display method of the recalculation result based on the estimated user's emotion. The recalculation unit, for example, estimates the user's emotion and adjusts the display method of the recalculation result based on the estimated user's emotion. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. For example, when the user is nervous, the recalculation unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the recalculation unit can provide a display method that includes detailed information. Furthermore, when the user is hurrying, the recalculation unit can provide a display method that focuses on the main points. This allows for adjusting the display method of the recalculation result according to the user's emotion, thereby providing more understandable information. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the recalculation unit may be performed using, for example, an AI, or without an AI. For example, the recalculation unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0129] The recalculation unit can recalculate the route by reflecting traffic congestion and accident information in real time during recalculation. For example, the recalculation unit recalculates the route by reflecting traffic congestion and accident information in real time during recalculation. Traffic congestion includes, for example, real-time traffic information and past traffic data, but is not limited to these examples. Accident information includes, for example, real-time traffic accident data and past accident data, but is not limited to these examples. For example, the recalculation unit reflects traffic congestion information in real time and recalculates the optimal route. The recalculation unit can also reflect accident information in real time and recalculate the optimal route. The recalculation unit can also integrate traffic congestion and accident information to recalculate the optimal route. In this way, the optimal route can be recalculated by reflecting traffic congestion and accident information in real time. Some or all of the above-described processing in the recalculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recalculation unit can input traffic congestion and accident information data into AI and cause the AI ​​to recalculate the optimal route.

[0130] The recalculation unit can recalculate the route by reflecting changes in the delivery destination's available pickup time during recalculation. For example, the recalculation unit recalculates the route by reflecting changes in the delivery destination's available pickup time during recalculation. Changes in available pickup time include, but are not limited to, changes in the customer's desired time slot and past pickup history. For example, the recalculation unit reflects changes in the delivery destination's available pickup time in real time and recalculates the optimal route. The recalculation unit can also compare multiple routes and select the optimal route, taking changes in available pickup time into consideration. The recalculation unit can also optimize the delivery order by reflecting changes in available pickup time. This enables efficient delivery by reflecting changes in the delivery destination's available pickup time. Some or all of the above-described processing by the recalculation unit may be performed using, or without, AI. For example, the recalculation unit can input data on changes in available pickup time into AI and have the AI ​​recalculate the optimal route.

[0131] The recalculation unit can propose an optimal refueling point by taking into account the truck's remaining fuel during recalculation. For example, the recalculation unit proposes an optimal refueling point by taking into account the truck's remaining fuel during recalculation. The remaining fuel may be, for example, a fuel gauge on the truck or a predicted remaining fuel amount based on the mileage, but is not limited to such examples. For example, the recalculation unit monitors the truck's remaining fuel in real time and proposes an optimal refueling point. The recalculation unit can also compare multiple refueling points and select the optimal point while taking into account the remaining fuel. The recalculation unit can also recalculate the optimal route by reflecting the remaining fuel. This enables efficient refueling by taking into account the truck's remaining fuel. Some or all of the above-described processing in the recalculation unit may be performed using, or without, AI. For example, the recalculation unit can input remaining fuel data into AI and have the AI ​​suggest the optimal refueling point.

[0132] The recalculation unit can estimate the user's emotions and determine the priority of recalculation based on the estimated user emotions. The recalculation unit, for example, estimates the user's emotions and determines the priority of recalculation based on the estimated user emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. For example, when the user is stressed, the recalculation unit quickly recalculates and proposes the optimal route. Furthermore, when the user is relaxed, the recalculation unit can perform detailed recalculation and propose multiple routes. Furthermore, when the user is in a hurry, the recalculation unit can prioritize recalculating the route that can be reached in the shortest time. This enables efficient recalculation by determining the priority of recalculation based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the recalculation unit may be performed using, for example, an AI, or without an AI. For example, the recalculation unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0133] The recalculation unit can recalculate the route taking into account weather information for the delivery destination when recalculating. For example, the recalculation unit recalculates the route taking into account weather information for the delivery destination when recalculating. Weather information includes, for example, data from the Japan Meteorological Agency and real-time weather information, but is not limited to these examples. For example, the recalculation unit reflects weather information for the delivery destination in real time and recalculates the optimal route. The recalculation unit can also compare multiple routes taking into account weather information and select the optimal route. The recalculation unit can also recalculate the optimal route taking into account weather information. This enables efficient delivery by taking into account weather information for the delivery destination. Some or all of the above-described processing by the recalculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recalculation unit can input weather information data into AI and have the AI ​​recalculate the optimal route.

[0134] The recalculation unit can recalculate the route taking into account traffic conditions at the delivery destination when recalculating. For example, the recalculation unit recalculates the route taking into account traffic conditions at the delivery destination when recalculating. Traffic conditions include, but are not limited to, real-time traffic information and past traffic data. For example, the recalculation unit reflects traffic conditions at the delivery destination in real time and recalculates the optimal route. The recalculation unit can also compare multiple routes taking into account traffic conditions and select the optimal route. The recalculation unit can also recalculate the optimal route taking into account traffic conditions. This enables efficient delivery by taking into account traffic conditions at the delivery destination. Some or all of the above-described processing in the recalculation unit may be performed using, or without, AI. For example, the recalculation unit can input traffic condition data into AI and have the AI ​​recalculate the optimal route.

[0135] The recalculation unit can recalculate the route taking into account the truck's maintenance status during recalculation. For example, the recalculation unit recalculates the route taking into account the truck's maintenance status during recalculation. The maintenance status includes, for example, a maintenance history and a next maintenance schedule, but is not limited to these examples. For example, the recalculation unit reflects the truck's maintenance status in real time and recalculates the optimal route. The recalculation unit can also compare multiple routes taking into account the maintenance status and select the optimal route. The recalculation unit can also recalculate the optimal route taking into account the maintenance status. This enables efficient operation by taking into account the truck's maintenance status. Some or all of the above-described processing in the recalculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recalculation unit can input maintenance status data into AI and have the AI ​​recalculate the optimal route. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, prediction unit, route calculation unit, inventory allocation unit, monitoring unit, and recalculation unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects demand data using the camera 42 and microphone 38B of the smart device 14 and processes the data using the control unit 46A. The prediction unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and performs demand forecasting. The route calculation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, calculates an optimal logistics route based on the predicted demand. The inventory allocation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, performs optimal inventory allocation. The monitoring unit, for example, uses the GPS function of the smart device 14 to obtain truck location information and monitor the progress of logistics in real time. The recalculation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, recalculates routes and reallocates inventory in response to changes in traffic conditions. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, prediction unit, route calculation unit, inventory allocation unit, monitoring unit, and recalculation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects demand data using the camera 42 and microphone 238 of the smart glasses 214 and processes the data using the control unit 46A. The prediction unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and performs demand forecasting. The route calculation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, calculates an optimal logistics route based on the predicted demand. The inventory allocation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, performs optimal inventory allocation. The monitoring unit, for example, uses the GPS function of the smart glasses 214 to obtain truck location information and monitor the progress of logistics in real time. The recalculation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, recalculates routes and reallocates inventory in response to changes in traffic conditions. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, prediction unit, route calculation unit, inventory allocation unit, monitoring unit, and recalculation unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects demand data using the camera 42 and microphone 238 of the headset terminal 314 and processes the data using the control unit 46A. The prediction unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data to perform demand forecasting. The route calculation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, calculates an optimal logistics route based on the predicted demand. The inventory allocation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, performs optimal inventory allocation. The monitoring unit, for example, uses the GPS function of the headset terminal 314 to obtain truck location information and monitor the progress of logistics in real time. The recalculation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, recalculates routes and reallocates inventory in response to changes in traffic conditions. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, prediction unit, route calculation unit, inventory allocation unit, monitoring unit, and recalculation unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects demand data using the camera 42 and microphone 238 of the robot 414 and processes the data using the control unit 46A. The prediction unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and performs demand forecasting. The route calculation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, calculates an optimal logistics route based on the predicted demand. The inventory allocation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, performs optimal inventory allocation. The monitoring unit, for example, uses the GPS function of the robot 414 to obtain truck location information and monitor the progress of logistics in real time. The recalculation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, recalculates routes and reallocates inventory in response to changes in traffic conditions.

[0136] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0137] The collection unit can estimate the user's emotions and adjust the type of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit collects only the minimum amount of data necessary to reduce the user's burden. Also, if the user is relaxed, the collection unit can collect detailed data. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting data that can be collected quickly. This allows for efficient data collection by adjusting the type of data to be collected according to the user's emotions.

[0138] The prediction unit can estimate the user's emotions and adjust the notification method of the prediction result based on the estimated user emotions. For example, if the user is feeling stressed, the prediction unit can provide a simple, highly visible notification method. If the user is relaxed, the prediction unit can provide a notification method that includes detailed information. Furthermore, if the user is in a hurry, the prediction unit can provide a notification method that focuses on the main points. In this way, by adjusting the notification method of the prediction result according to the user's emotions, it is possible to provide information that is easier to understand.

[0139] The route calculation unit can estimate the user's emotions and adjust the frequency of route calculation based on the estimated user emotions. For example, if the user is feeling stressed, the route calculation unit can frequently recalculate the route and provide an optimal route. If the user is relaxed, the route calculation unit can reduce the frequency of route calculation and provide a stable route. Furthermore, if the user is in a hurry, the route can be quickly recalculated and a route that can be reached in the shortest time can be provided. In this way, adjusting the frequency of route calculation according to the user's emotions makes it possible to provide efficient routes.

[0140] The inventory allocation unit can estimate the user's emotions and determine the priority of inventory allocation based on the estimated user's emotions. For example, if the user is feeling stressed, it can prioritize allocation of important inventory. Also, if the user is relaxed, it can perform detailed inventory allocation. Furthermore, if the user is in a hurry, it can perform quick inventory allocation. In this way, efficient inventory management is possible by determining the priority of inventory allocation according to the user's emotions.

[0141] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. For example, if the user is nervous, a simple, 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 focuses on the main points can be provided. In this way, by adjusting the display method of the monitoring results according to the user's emotions, it is possible to provide information that is easier to understand.

[0142] The collection unit can collect social media trend data in addition to past sales data and seasonal demand fluctuations. For example, the collection unit analyzes hashtag frequency and post content and reflects the results in demand forecasts. The collection unit can also analyze social media post content to predict demand fluctuations. Furthermore, the collection unit can integrate social media trend data with other data to make more accurate demand forecasts. In this way, collecting social media trend data improves the accuracy of demand forecasts.

[0143] The prediction unit can improve prediction accuracy by combining different prediction algorithms during prediction. For example, the prediction unit can improve prediction accuracy by combining time series analysis and a machine learning model. The prediction unit can also compare multiple prediction algorithms and select the algorithm with the highest accuracy. Furthermore, the prediction unit can improve the reliability of the prediction results by combining different prediction algorithms. As a result, prediction accuracy is improved by combining different prediction algorithms.

[0144] The route calculation unit can optimize the route by taking into account the risk of traffic accidents and natural disasters when calculating the route. For example, the route calculation unit can propose the optimal route by taking into account real-time data on traffic accidents and past accident data. The route calculation unit can also propose the optimal route by taking into account the risk of natural disasters. Furthermore, the route calculation unit can also propose the optimal route by integrating the risk of traffic accidents and natural disasters. This makes it possible to propose a safer route by taking into account the risk of traffic accidents and natural disasters.

[0145] The inventory allocation unit can optimize inventory allocation by taking into account product storage conditions when allocating inventory. For example, the inventory allocation unit proposes optimal inventory allocation by taking into account product temperature, humidity, and light conditions. The inventory allocation unit can also optimize product allocation by taking into account temperature and humidity. Furthermore, the inventory allocation unit can integrate storage conditions and propose optimal inventory allocation. This makes it possible to optimize inventory allocation by taking into account product storage conditions.

[0146] During monitoring, the monitoring unit can monitor the truck's fuel consumption and driving time to support efficient operation. For example, the monitoring unit can monitor the truck's fuel consumption data and consumption based on the mileage to support efficient operation. The monitoring unit can also monitor the driving start time, rest time, and driving end time to support efficient operation. Furthermore, the monitoring unit can integrate the fuel consumption and driving time to support efficient operation. In this way, efficient operation is possible by monitoring the truck's fuel consumption and driving time.

[0147] The processing flow of the second embodiment will be briefly explained below.

[0148] Step 1: The collection unit collects demand data. Demand data includes sales data, customer behavior data, and market trend data. The collection unit collects past sales data, seasonal demand fluctuation data, daily sales data, and monthly sales data. Step 2: The forecasting unit performs demand forecasting based on the data collected by the collection unit. The forecasting unit analyzes past data using time series analysis, regression analysis, and machine learning models to predict future demand. Step 3: The route calculation unit calculates the optimal logistics route based on the demand predicted by the forecasting unit. The route calculation unit takes into account traffic conditions and inventory placement, and calculates the route based on minimizing distance, time, and cost. Step 4: The inventory allocation unit optimizes inventory allocation based on the route calculated by the route calculation unit. The inventory allocation unit allocates more inventory in areas with high demand and less inventory in areas with low demand. Step 5: The monitoring unit monitors the progress of the inventory allocated by the inventory allocation unit in real time. The monitoring unit obtains the location information of the truck using GPS and grasps the progress of logistics. Step 6: The recalculation unit recalculates routes and reallocates inventory based on the progress monitored by the monitoring unit. If a traffic jam occurs, the recalculation unit calculates a new route and issues instructions to the truck. The route is recalculated according to changes in traffic conditions.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0180] 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.

[0181] 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.

[0182] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0183] 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.

[0184] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0185] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0197] 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.

[0198] 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.

[0199] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0200] 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.

[0201] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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).

[0206] 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.

[0207] 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."

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] [Explanation of symbols]

[0221] 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 collection unit that collects demand data; a forecasting unit that performs demand forecasting based on the data collected by the collecting unit; a route calculation unit that calculates an efficient logistics route based on the demand predicted by the prediction unit; an inventory allocation unit that improves the efficiency of inventory allocation based on the route calculated by the route calculation unit; a monitoring unit that monitors in real time the progress of the inventory allocated by the inventory allocation unit; a recalculation unit that recalculates routes and rearranges inventory based on the progress monitored by the monitoring unit. A system characterized by:

2. The collecting unit Collect historical sales data or seasonal demand fluctuations 2. The system of claim 1.

3. The prediction unit Analyzing collected data and predicting future demand 2. The system of claim 1.

4. The route calculation unit Calculate the optimal logistics route taking into account traffic conditions and inventory placement 2. The system of claim 1.

5. The inventory placement unit Place more inventory in areas with high demand and less inventory in areas with low demand 2. The system of claim 1.

6. The monitoring unit Use GPS to obtain truck location information and track logistics progress 2. The system of claim 1.

7. The recalculation unit Calculates new routes and gives instructions to trucks when traffic jams occur 2. The system of claim 1.

8. The collecting unit To estimate user emotions and adjust the timing of demand data collection based on the estimated user emotions.

2. The system of claim 1.

Images