system

Abstract

The system according to this embodiment aims to eliminate the time lag between offline and online operations through logistics optimization and demand forecasting. [Solution] The system according to the embodiment comprises a logistics analysis unit, a delivery route selection unit, a demand forecasting unit, an inventory management unit, and a recommendation unit. The logistics analysis unit analyzes each process of logistics. The delivery route selection unit selects a delivery route based on the information analyzed by the logistics analysis unit. The demand forecasting unit analyzes the user's purchase history or behavioral data. The inventory management unit manages inventory based on the forecast results obtained by the demand forecasting unit. The recommendation unit analyzes the user's behavioral data and suggests products.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the optimization of logistics and demand forecasting are not sufficiently performed, and a time difference occurs between offline and online.

[0005] The system according to the embodiment aims to eliminate the time difference between offline and online through the optimization of logistics and demand forecasting.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a logistics analysis unit, a delivery route selection unit, a demand forecasting unit, an inventory management unit, and a recommendation unit. The logistics analysis unit analyzes each process of logistics. The delivery route selection unit selects a delivery route based on the information analyzed by the logistics analysis unit. The demand forecasting unit analyzes the user's purchase history or behavioral data. The inventory management unit manages inventory based on the forecast results obtained by the demand forecasting unit. The recommendation unit analyzes the user's behavioral data and suggests products. [Effects of the Invention]

[0007] The system according to this embodiment can eliminate the time lag between offline and online operations through logistics optimization and demand forecasting. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The logistics optimization system according to an embodiment of the present invention is a system that analyzes each process of logistics and proposes the optimal route and schedule. The aim of this system is to shorten logistics lead times and bring user lead times as close to zero as possible. Specifically, it comprises a logistics analysis unit that analyzes each process of logistics, a delivery route selection unit that selects a delivery route based on the analyzed information, a demand forecasting unit that analyzes users' purchase history and behavioral data, an inventory management unit that manages inventory based on the forecast results, and a recommendation unit that analyzes user behavioral data and proposes appropriate products. For example, the logistics analysis unit monitors the inventory status of products and the congestion status of delivery routes in real time and selects the optimal delivery route. This shortens the delivery time of products and reduces the time from when a user wants a product until they actually receive it. Next, the demand forecasting unit analyzes users' purchase history and behavioral data and predicts future demand. For example, it predicts how much of a particular product will be sold and when, and manages inventory appropriately. This prevents product shortages and allows users to purchase products immediately when they want them. Furthermore, the recommendation unit analyzes user behavioral data and proposes related products. For example, the system recommends related products based on the user's past purchases and browsing history. This shortens the time it takes for the user to click the purchase button, eliminating the time lag between online and offline. This system can bring logistics lead times and user lead times as close to zero as possible, eliminating the time lag between the offline and online worlds. In this way, the logistics optimization system can shorten logistics lead times and user lead times, eliminating the time lag between offline and online.

[0029] The logistics optimization system according to this embodiment comprises a logistics analysis unit, a delivery route selection unit, a demand forecasting unit, an inventory management unit, and a recommendation unit. The logistics analysis unit analyzes each process of logistics. Each process of logistics includes, but is not limited to, order processing, picking, packing, and shipping. The logistics analysis unit analyzes each process of logistics using AI, for example, and proposes the optimal route and schedule. For example, the logistics analysis unit monitors the inventory status of products and the congestion status of delivery routes in real time and selects the optimal delivery route. The delivery route selection unit selects a delivery route based on the information analyzed by the logistics analysis unit. For example, the delivery route selection unit selects the optimal delivery route based on criteria such as distance, time, and cost. The demand forecasting unit analyzes users' purchase history and behavioral data and forecasts future demand. For example, the demand forecasting unit forecasts how much of a particular product will be sold and when, and manages inventory appropriately. The demand forecasting unit analyzes users' purchase history and behavioral data using AI and forecasts future demand. For example, the demand forecasting department predicts when and how much of a particular product will sell based on users' purchase history and behavioral data. The inventory management department manages inventory based on the forecast results obtained by the demand forecasting department. The inventory management department manages inventory based on criteria such as how to set inventory levels and when to replenish. The recommendation department analyzes user behavioral data and suggests appropriate products. The recommendation department recommends related products based on products that users have previously purchased and their browsing history. As a result, the logistics optimization system can shorten logistics lead times and user lead times, and eliminate the time lag between offline and online.

[0030] The Logistics Analytics Department analyzes each process in logistics. These processes include, but are not limited to, order processing, picking, packing, and shipping. For example, the Logistics Analytics Department uses AI to analyze each process and propose optimal routes and schedules. Specifically, at the order processing stage, the department collects order data, and AI analyzes this data to determine which warehouse is most efficient for shipping which products. At the picking stage, AI optimizes product placement and picking routes, enabling workers to collect products in the shortest possible time. At the packing stage, AI proposes the optimal packing method based on the shape and size of the products, reducing waste of packaging materials. At the shipping stage, AI monitors congestion and traffic information on delivery routes in real time and proposes the optimal shipping schedule. This allows the Logistics Analytics Department to streamline each process in logistics and shorten overall lead times. Furthermore, based on historical data and trends, the Logistics Analytics Department can predict future logistics demand and take proactive measures. For example, it can predict increased demand during specific seasons or events and secure the necessary resources in advance. Furthermore, the logistics analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data early and respond quickly. This allows the logistics analysis department to contribute not only to logistics efficiency but also to risk management and anomaly response, thereby improving the overall reliability and security of the system.

[0031] The delivery route selection unit selects delivery routes based on information analyzed by the logistics analysis unit. The unit selects the optimal delivery route based on criteria such as distance, time, and cost. Specifically, it uses AI to simulate multiple delivery routes and select the most efficient one. The AI ​​analyzes traffic information, weather information, and road congestion in real time to calculate the optimal route. For example, the AI ​​can suggest a route that uses back roads instead of main roads to avoid traffic congestion. The delivery route selection unit also considers factors such as the fuel efficiency of delivery vehicles and the working hours of drivers to select the most cost-effective route. This allows the delivery route selection unit to simultaneously achieve shorter delivery times and cost reductions. Furthermore, when there are multiple delivery destinations, the delivery route selection unit can calculate the optimal delivery order and propose an efficient route. For example, the AI ​​considers the geographical location of the destinations and delivery time constraints to calculate a route that visits all destinations via the shortest distance. The delivery route selection unit can also recalculate routes based on real-time updated information, allowing it to flexibly adapt to changing circumstances. This allows the delivery route selection unit to always provide the optimal route, improving the efficiency and reliability of deliveries.

[0032] The demand forecasting department analyzes users' purchase history and behavioral data to predict future demand. For example, it predicts how much of a particular product will sell and when, and manages inventory appropriately. Specifically, the demand forecasting department uses AI to analyze users' purchase history and behavioral data to predict future demand. Based on past purchase data, search history, and browsing history, the AI ​​analyzes users' purchasing patterns and predicts how much of a particular product will sell and when. For example, the AI ​​predicts an increase in demand during a specific season or event based on past data and secures inventory in advance. Furthermore, the demand forecasting department can improve the accuracy of its demand forecasts by utilizing external data sources. For example, it can analyze weather data, economic indicators, and social media trends to identify factors that cause fluctuations in demand. This allows the demand forecasting department to make more accurate demand forecasts and prevent inventory surpluses and shortages. In addition, the demand forecasting department can continuously revise its demand forecasts based on real-time updated data to respond to the latest situations. For example, if a particular product suddenly starts selling well, the demand forecasting department immediately incorporates new data and updates the forecast results. Furthermore, the demand forecasting unit can use anomaly detection algorithms to detect unusual patterns and abnormal data early on, enabling rapid response. This allows the demand forecasting unit to always provide highly accurate demand forecasts based on the latest information, improving the efficiency and reliability of inventory management.

[0033] The Inventory Management Department manages inventory based on forecast results obtained by the Demand Forecasting Department. The Inventory Management Department manages inventory based on criteria such as how to set inventory levels and replenishment timing. Specifically, the Inventory Management Department uses AI to optimize inventory levels and achieve inventory management without surpluses or shortages. Based on forecast data provided by the Demand Forecasting Department, the AI ​​calculates the optimal inventory level for each product and proposes appropriate replenishment timing. For example, the AI ​​replenishes inventory before a particular product starts selling well and reduces inventory at the appropriate time to prevent unsold stock. The Inventory Management Department also plans the placement and movement of products to optimize the use of warehouse space. This enables the Inventory Management Department to achieve efficient warehouse operation and reduce costs. Furthermore, the Inventory Management Department can continuously monitor inventory status based on real-time updated inventory data and respond quickly as needed. For example, if a particular product suddenly starts selling well, the Inventory Management Department immediately incorporates the new data and adjusts the inventory level. The Inventory Management Department also uses anomaly detection algorithms to detect unusual patterns and abnormal data early and respond quickly. This allows the inventory management department to provide highly accurate inventory management based on the latest information at all times, preventing inventory shortages and surpluses and enabling efficient operations.

[0034] The recommendation department analyzes user behavior data and suggests appropriate products. For example, it recommends related products based on a user's past purchases and browsing history. Specifically, the recommendation department uses AI to analyze user behavior data and suggest products that are best suited to each individual user. The AI ​​analyzes the user's preferences and interests based on past purchase history, browsing history, search history, etc., and recommends related products. For example, the AI ​​suggests accessories and complementary products related to a user who has purchased a particular product. Furthermore, the recommendation department can analyze user behavior data in real time and provide timely recommendations. For example, it can instantly suggest related products while a user is browsing a particular product. In addition, the recommendation department can utilize external data sources to improve the accuracy of recommendations. For example, it can analyze social media trends and reviews on review sites to suggest popular and highly-rated products. This allows the recommendation department to provide more personalized product suggestions to users and increase their purchase intent. Furthermore, the recommendation department can collect user feedback and continuously improve the accuracy of its recommendation algorithms. For example, it can evaluate the effectiveness of recommendations and adjust the algorithms based on data showing whether users purchased the suggested products. This allows the recommendation department to always provide highly accurate product suggestions based on the latest information, thereby improving user satisfaction.

[0035] The logistics analysis department can monitor product inventory status or delivery route congestion in real time and select delivery routes. For example, the logistics analysis department can monitor product inventory status in real time. For example, the logistics analysis department monitors real-time updates of inventory levels and inventory types. The logistics analysis department can also monitor delivery route congestion in real time. For example, the logistics analysis department monitors traffic conditions and the operational status of distribution centers. This allows the logistics analysis department to select the optimal delivery route and shorten delivery times. Some or all of the above processes in the logistics analysis department may be performed using AI, for example, or not using AI. For example, the logistics analysis department can input inventory status and congestion data into AI and have the AI ​​select the optimal delivery route.

[0036] The demand forecasting unit can analyze users' purchase history and behavioral data to predict future demand. For example, the demand forecasting unit analyzes users' purchase history. For example, it analyzes data such as purchase date and time, purchased items, and purchase frequency. The demand forecasting unit can also analyze users' behavioral data. For example, it analyzes data such as website browsing history and click history. This allows the demand forecasting unit to accurately predict future demand. Some or all of the above processing in the demand forecasting unit may be performed using AI, for example, or without AI. For example, the demand forecasting unit can input purchase history and behavioral data into AI and have the AI ​​perform the prediction of future demand.

[0037] The inventory management department can appropriately manage inventory based on the forecast results obtained by the demand forecasting department. The inventory management department manages inventory based on criteria such as how to set inventory levels and when to replenish stock. For example, the inventory management department sets inventory levels based on the forecast results obtained by the demand forecasting department and replenishes inventory at the appropriate time. This allows the inventory management department to prevent stockouts and enable proper inventory management. Some or all of the above processes in the inventory management department may be performed using AI, for example, or not using AI. For example, the inventory management department can input forecast results into AI and have the AI ​​perform inventory management optimization.

[0038] The recommendation unit can analyze user behavior data and suggest products. For example, the recommendation unit analyzes user behavior data, such as website browsing history and click history. The recommendation unit can also suggest related products based on products the user has purchased in the past. For example, the recommendation unit recommends related products based on products the user has purchased in the past and their browsing history. This allows the recommendation unit to shorten the time it takes for the user to click the purchase button. Some or all of the above processes in the recommendation unit may be performed using AI, for example, or not. For example, the recommendation unit can input behavior data into AI and have the AI ​​optimize product suggestions.

[0039] The logistics analysis unit can select an analysis algorithm by referring to past delivery data during logistics analysis. For example, the logistics analysis unit can refer to past delivery data. For example, the logistics analysis unit can refer to data such as delivery date and time, delivery route, and delivery time. The logistics analysis unit can also apply an algorithm to select the most efficient delivery route based on past delivery data. For example, the logistics analysis unit can select the optimal delivery route for a specific season or time of day from past delivery data. The logistics analysis unit can also analyze past delivery data and select an algorithm to avoid frequently occurring problems. This allows the logistics analysis unit to select the optimal analysis algorithm and improve the accuracy of the analysis. Some or all of the above processes in the logistics analysis unit may be performed using AI, for example, or without AI. For example, the logistics analysis unit can input past delivery data into AI and have the AI ​​select the optimal analysis algorithm.

[0040] The logistics analysis department can apply different analysis methods depending on the characteristics of the product during logistics analysis. For example, in the case of fresh food, temperature control is important, so the logistics analysis department incorporates temperature sensor data into the analysis. For example, the logistics analysis department selects a delivery route for products that require temperature control based on the temperature sensor data. Furthermore, in the case of expensive products, the logistics analysis department can also apply analysis methods that prioritize security. For example, the logistics analysis department considers security risks when selecting a delivery route for expensive products. In addition, when handling a large volume of products, the logistics analysis department can apply analysis methods that perform efficient batch processing. For example, the logistics analysis department applies a batch processing algorithm to efficiently deliver a large volume of products. This allows the logistics analysis department to apply analysis methods appropriate to the characteristics of the product and improve the accuracy of the analysis. Some or all of the above processes in the logistics analysis department may be performed using AI, for example, or without AI. For example, the logistics analysis department can input product characteristic data into AI and have the AI ​​select the optimal analysis method.

[0041] The logistics analysis department can improve the accuracy of its analysis based on geographical factors during logistics analysis. For example, the logistics analysis department considers geographical factors. For example, the logistics analysis department applies analysis methods that are appropriate for the characteristics of mountainous areas or urban areas. The logistics analysis department can also apply analysis methods that minimize the impact of traffic congestion and road construction. For example, the logistics analysis department selects the optimal delivery route based on traffic congestion and road construction data. Furthermore, the logistics analysis department can apply analysis methods that respond to seasonal and weather fluctuations. For example, the logistics analysis department selects the optimal delivery route based on seasonal and weather data. In this way, the logistics analysis department can improve the accuracy of its analysis by considering geographical factors. Some or all of the above processes in the logistics analysis department may be performed using AI, for example, or without AI. For example, the logistics analysis department can input geographical factor data into AI and have the AI ​​perform the analysis accuracy improvement.

[0042] The logistics analysis unit can improve the accuracy of its analysis by referring to relevant external data during logistics analysis. For example, the logistics analysis unit can refer to external traffic information data. For example, the logistics analysis unit can select the optimal delivery route based on traffic information data. The logistics analysis unit can also refer to external weather data and perform analysis that takes weather effects into account. For example, the logistics analysis unit can select a delivery route that minimizes the impact of weather based on weather data. Furthermore, the logistics analysis unit can refer to external market data and perform analysis that responds to fluctuations in demand. For example, the logistics analysis unit can predict fluctuations in demand based on market data and select the optimal delivery route. In this way, the logistics analysis unit can improve the accuracy of its analysis by referring to relevant external data. Some or all of the above processes in the logistics analysis unit may be performed using AI, for example, or without AI. For example, the logistics analysis unit can input external data into AI and have AI perform the task of improving the accuracy of the analysis.

[0043] The delivery route selection unit can select a route by referring to past delivery history when selecting a delivery route. For example, the delivery route selection unit can refer to past delivery history. For example, the delivery route selection unit can refer to data such as delivery date and time, delivery route, and delivery time. The delivery route selection unit can also select the most efficient route based on past delivery history. For example, the delivery route selection unit can select the optimal route for a specific season or time of day from past delivery history. The delivery route selection unit can also analyze past delivery history and select a route that avoids frequently occurring problems. In this way, the delivery route selection unit can select the optimal route and improve delivery efficiency. Some or all of the above processing in the delivery route selection unit may be performed using AI, for example, or without AI. For example, the delivery route selection unit can input past delivery history into AI and have the AI ​​perform the selection of the optimal route.

[0044] The delivery route selection unit can apply different route selection algorithms depending on the characteristics of the product when selecting a delivery route. For example, in the case of fresh food, temperature control is important, so the delivery route selection unit selects a route that takes temperature sensor data into consideration. For example, the delivery route selection unit selects a delivery route for products that require temperature control based on temperature sensor data. In addition, the delivery route selection unit can also select a route that prioritizes security in the case of expensive products. For example, the delivery route selection unit considers security risks when selecting a delivery route for expensive products. Furthermore, when handling a large volume of products, the delivery route selection unit can perform route selection that performs efficient batch processing. For example, the delivery route selection unit applies a batch processing algorithm to efficiently deliver a large volume of products. In this way, the delivery route selection unit can apply a route selection algorithm that is appropriate for the characteristics of the product and improve the accuracy of delivery. Some or all of the above processing in the delivery route selection unit may be performed using AI, for example, or without using AI. For example, the delivery route selection unit can input product characteristic data into AI and have the AI ​​execute the application of the optimal route selection algorithm.

[0045] The delivery route selection unit can improve the accuracy of route selection based on geographical factors during the delivery route selection process. For example, the delivery route selection unit considers geographical factors. For example, it selects routes according to the characteristics of mountainous areas and urban areas. The delivery route selection unit can also select routes that minimize the impact of traffic congestion and road construction. For example, it selects the optimal delivery route based on traffic congestion and road construction data. Furthermore, the delivery route selection unit can select routes that respond to seasonal and weather fluctuations. For example, it selects the optimal delivery route based on seasonal and weather data. In this way, the delivery route selection unit can improve the accuracy of route selection by considering geographical factors. Some or all of the above processing in the delivery route selection unit may be performed using AI, for example, or without AI. For example, the delivery route selection unit can input geographical factor data into AI and have the AI ​​perform the route selection accuracy improvement.

[0046] The delivery route selection unit can improve the accuracy of route selection by referring to relevant external data when selecting a delivery route. For example, the delivery route selection unit can refer to external traffic information data. For example, the delivery route selection unit can select the optimal delivery route based on traffic information data. The delivery route selection unit can also refer to external weather data and select a route that takes weather effects into account. For example, the delivery route selection unit can select a delivery route that minimizes the impact of weather based on weather data. Furthermore, the delivery route selection unit can refer to external market data and select a route that responds to fluctuations in demand. For example, the delivery route selection unit can predict fluctuations in demand based on market data and select the optimal delivery route. In this way, the delivery route selection unit can improve the accuracy of route selection by referring to relevant external data. Some or all of the above processing in the delivery route selection unit may be performed using AI, for example, or without using AI. For example, the delivery route selection unit can input external data into AI and have the AI ​​perform the task of improving the accuracy of route selection.

[0047] The demand forecasting unit can select a forecasting algorithm by referring to past purchase data when forecasting demand. For example, the demand forecasting unit may refer to past purchase data such as purchase date and time, purchased items, and purchase frequency. The demand forecasting unit can also select the most accurate forecasting algorithm based on past purchase data. For example, the demand forecasting unit may select an algorithm that forecasts demand in a specific season or event from past purchase data. The demand forecasting unit may also analyze past purchase data and select an algorithm that forecasts frequently occurring fluctuations in demand. In this way, the demand forecasting unit can select the optimal forecasting algorithm and improve the accuracy of demand forecasting. Some or all of the above processing in the demand forecasting unit may be performed using AI, for example, or not using AI. For example, the demand forecasting unit can input past purchase data into AI and have the AI ​​select the optimal forecasting algorithm.

[0048] The demand forecasting unit can apply different forecasting methods depending on the characteristics of the product when forecasting demand. For example, in the case of fresh food, the demand forecasting unit can apply a demand forecasting method that takes into account the expiration date. For example, the demand forecasting unit forecasts the demand for fresh food based on the expiration date. The demand forecasting unit can also apply a demand forecasting method that takes into account the purchase frequency in the case of expensive products. For example, the demand forecasting unit forecasts the demand based on the purchase frequency of expensive products. Furthermore, when dealing with a large volume of products, the demand forecasting unit can also apply a demand forecasting method that takes batch processing into account. For example, the demand forecasting unit can apply a batch processing algorithm to efficiently manage a large volume of products. This allows the demand forecasting unit to apply forecasting methods appropriate to the characteristics of the product and improve the accuracy of demand forecasting. Some or all of the above processing in the demand forecasting unit may be performed using AI, for example, or without AI. For example, the demand forecasting unit can input product characteristic data into AI and have the AI ​​apply the optimal forecasting method.

[0049] The demand forecasting unit can improve the accuracy of its forecasts based on geographical factors during the demand forecasting process. For example, the demand forecasting unit considers geographical factors. For example, the demand forecasting unit applies a method for forecasting demand in a specific region. The demand forecasting unit can also apply demand forecasting methods that respond to seasonal and weather fluctuations. For example, the demand forecasting unit forecasts demand in a specific region based on seasonal and weather data. Furthermore, the demand forecasting unit can apply a method for analyzing purchasing patterns by region. For example, the demand forecasting unit forecasts demand based on purchasing data by region. This allows the demand forecasting unit to improve the accuracy of its forecasts by considering geographical factors. Some or all of the above processing in the demand forecasting unit may be performed using AI, for example, or without AI. For example, the demand forecasting unit can input geographical factor data into AI and have AI perform the task of improving the accuracy of the forecast.

[0050] The demand forecasting unit can improve the accuracy of its forecasts by referring to relevant external data during the demand forecasting process. For example, the demand forecasting unit may refer to external market data. For example, the demand forecasting unit may make forecasts that respond to fluctuations in demand based on market data. The demand forecasting unit can also refer to external economic data and make demand forecasts that correspond to economic conditions. For example, the demand forecasting unit may make demand forecasts that correspond to economic conditions based on economic data. Furthermore, the demand forecasting unit may refer to external consumer data and analyze consumer purchasing patterns. For example, the demand forecasting unit may analyze consumer purchasing patterns based on consumer data and forecast demand. In this way, the demand forecasting unit can improve the accuracy of its forecasts by referring to relevant external data. Some or all of the above processes in the demand forecasting unit may be performed using AI, for example, or not using AI. For example, the demand forecasting unit may input external data into AI and have AI perform the task of improving the accuracy of the forecast.

[0051] The inventory management department can select inventory management methods by referring to historical inventory data during inventory management. For example, the inventory management department can refer to historical inventory data such as inventory levels, replenishment timing, and inventory turnover rate. The inventory management department can also select the most efficient inventory management method based on historical inventory data. For example, the inventory management department can select inventory management methods for specific seasons or events from historical inventory data. The inventory management department can also analyze historical inventory data and select management methods to avoid frequent inventory shortages. This allows the inventory management department to select the optimal management method and improve the accuracy of inventory management. Some or all of the above processes in the inventory management department may be performed using AI, for example, or not. For example, the inventory management department can input historical inventory data into AI and have the AI ​​select the optimal management method.

[0052] The inventory management department can apply different management methods depending on the characteristics of the product when managing inventory. For example, in the case of fresh food, the inventory management department can apply an inventory management method that takes expiration dates into consideration. For example, the inventory management department manages the inventory of fresh food based on its expiration date. The inventory management department can also apply an inventory management method that prioritizes security when dealing with expensive products. For example, the inventory management department considers security risks when managing the inventory of expensive products. Furthermore, when handling large quantities of products, the inventory management department can apply an inventory management method that performs efficient batch processing. For example, the inventory management department can apply a batch processing algorithm to efficiently manage large quantities of products. This allows the inventory management department to apply management methods tailored to the characteristics of the product and improve the accuracy of inventory management. Some or all of the above processes in the inventory management department may be performed using AI, for example, or not. For example, the inventory management department can input product characteristic data into AI and have the AI ​​execute the application of the optimal management method.

[0053] The inventory management department can improve the accuracy of its inventory management based on geographical factors. For example, the inventory management department can consider geographical factors. For example, the inventory management department can apply inventory management methods specific to a particular region. The inventory management department can also apply inventory management methods that respond to seasonal and weather fluctuations. For example, the inventory management department can manage inventory in a particular region based on seasonal and weather data. Furthermore, the inventory management department can apply methods to analyze regional demand patterns. For example, the inventory management department can manage inventory based on regional demand data. This allows the inventory management department to improve the accuracy of its management by considering geographical factors. Some or all of the above processes in the inventory management department may be performed using AI, for example, or not. For example, the inventory management department can input geographical factor data into AI and have the AI ​​perform the improvement of management accuracy.

[0054] The inventory management department can improve the accuracy of its inventory management by referring to relevant external data. For example, the inventory management department can refer to external market data. For example, the inventory management department can perform inventory management in response to fluctuations in demand based on market data. The inventory management department can also refer to external economic data and perform inventory management according to economic conditions. For example, the inventory management department can perform inventory management according to economic conditions based on economic data. Furthermore, the inventory management department can refer to external consumer data and analyze consumer purchasing patterns. For example, the inventory management department can analyze consumer purchasing patterns based on consumer data and manage inventory accordingly. In this way, the inventory management department can improve the accuracy of its management by referring to relevant external data. Some or all of the above processes in the inventory management department may be performed using AI, for example, or not using AI. For example, the inventory management department can input external data into AI and have the AI ​​perform the improvement of management accuracy.

[0055] The recommendation unit can select a recommendation algorithm by referring to past purchase data when making recommendations. For example, the recommendation unit may refer to past purchase data such as purchase date and time, purchased items, and purchase frequency. The recommendation unit can also select the most accurate recommendation algorithm based on past purchase data. For example, the recommendation unit may select the optimal recommendation algorithm for a specific season or event from past purchase data. The recommendation unit can also analyze past purchase data and select a recommendation algorithm for frequently purchased items. This allows the recommendation unit to select the optimal recommendation algorithm and improve the accuracy of recommendations. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input past purchase data into AI and have the AI ​​select the optimal recommendation algorithm.

[0056] The recommendation unit can apply different recommendation methods depending on the characteristics of the product during the recommendation process. For example, in the case of fresh food, the recommendation unit can apply a recommendation method that takes the expiration date into consideration. For example, the recommendation unit can recommend fresh food based on its expiration date. The recommendation unit can also apply a recommendation method that takes the purchase frequency into consideration for expensive products. For example, the recommendation unit can make recommendations based on the purchase frequency of expensive products. Furthermore, when handling a large number of products, the recommendation unit can apply a recommendation method that takes batch processing into consideration. For example, the recommendation unit can apply a batch processing algorithm to efficiently manage a large number of products. This allows the recommendation unit to apply a recommendation method that is appropriate to the characteristics of the product and improve the accuracy of recommendations. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without using AI. For example, the recommendation system can input product characteristic data into the AI ​​and have the AI ​​apply the most suitable recommendation method.

[0057] The recommendation unit can improve the accuracy of its recommendations based on geographical factors. For example, the recommendation unit considers geographical factors. For example, the recommendation unit applies a method to predict demand in a specific region. The recommendation unit can also apply recommendation methods that respond to seasonal and weather fluctuations. For example, the recommendation unit predicts demand in a specific region based on seasonal and weather data. Furthermore, the recommendation unit can apply a method to analyze purchasing patterns by region. For example, the recommendation unit predicts demand based on purchasing data by region. This allows the recommendation unit to improve the accuracy of its recommendations by considering geographical factors. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input geographical factor data into AI and have the AI ​​perform the recommendation accuracy improvement.

[0058] The recommendation unit can improve the accuracy of its recommendations by referring to relevant external data during the recommendation process. For example, the recommendation unit may refer to external market data. For example, the recommendation unit may make recommendations that respond to fluctuations in demand based on market data. The recommendation unit can also refer to external economic data and make recommendations that are appropriate to the economic situation. For example, the recommendation unit may make recommendations that are appropriate to the economic situation based on economic data. Furthermore, the recommendation unit may refer to external consumer data and analyze consumer purchasing patterns. For example, the recommendation unit may analyze consumer purchasing patterns based on consumer data and make recommendations. This allows the recommendation unit to improve the accuracy of its recommendations by referring to relevant external data. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit may input external data into AI and have the AI ​​perform the task of improving the accuracy of its recommendations.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The logistics analysis department can apply analytical methods to minimize energy consumption when analyzing logistics processes. For example, it can monitor the energy consumption of each delivery route in real time and select the most energy-efficient route. Furthermore, it can optimize delivery schedules based on energy consumption data. In addition, it can propose new delivery methods aimed at reducing energy consumption. Through these efforts, the logistics analysis department can minimize energy consumption and reduce its environmental impact.

[0061] The inventory management department can apply management methods that consider the product lifecycle when managing inventory. For example, the inventory management department can set inventory levels based on product lifecycle data from manufacturing to disposal. Furthermore, the inventory management department can adjust replenishment timing according to the product lifecycle stage. In addition, the inventory management department can optimize inventory based on product lifecycle data. This enables the inventory management department to manage inventory while considering the product lifecycle.

[0062] The Logistics Analysis Department can apply analytical methods that take environmental data into consideration when analyzing logistics processes. For example, the Logistics Analysis Department can monitor the environmental impact of each delivery route in real time and select routes with less environmental impact. Furthermore, the Logistics Analysis Department can optimize delivery schedules based on environmental data. In addition, the Logistics Analysis Department can propose new delivery methods aimed at reducing environmental impact. In this way, the Logistics Analysis Department can reduce environmental impact by applying analytical methods that take environmental data into consideration.

[0063] The inventory management department can apply management methods that take into account product consumption patterns when managing inventory. For example, the inventory management department can set inventory levels based on product consumption pattern data. Furthermore, the inventory management department can adjust replenishment timing according to product consumption patterns. In addition, the inventory management department can optimize inventory based on product consumption pattern data. This enables the inventory management department to manage inventory while considering product consumption patterns.

[0064] The logistics analysis department can apply analytical methods that take cost data into consideration when analyzing logistics processes. For example, the logistics analysis department can monitor the cost of each delivery route in real time and select the most cost-effective route. Furthermore, the logistics analysis department can optimize delivery schedules based on cost data. In addition, the logistics analysis department can propose new delivery methods aimed at cost reduction. In this way, the logistics analysis department can improve cost efficiency by applying analytical methods that take cost data into consideration.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The Logistics Analysis Department analyzes each process in logistics. These processes include order processing, picking, packing, and shipping. The Logistics Analysis Department uses AI to analyze each process and propose the optimal route and schedule. For example, it monitors product inventory status and delivery route congestion in real time to select the optimal delivery route. Step 2: The delivery route selection unit selects a delivery route based on the information analyzed by the logistics analysis unit. The delivery route selection unit selects the optimal delivery route based on criteria such as distance, time, and cost. Step 3: The demand forecasting unit analyzes users' purchase history and behavioral data to predict future demand. The demand forecasting unit predicts when and how much of a specific product will sell, providing information for proper inventory management. AI is used to analyze users' purchase history and behavioral data to predict future demand. Step 4: The Inventory Management Department manages inventory based on the forecast results obtained by the Demand Forecasting Department. The Inventory Management Department manages inventory based on criteria such as how to set inventory levels and when to replenish. Step 5: The recommendation team analyzes user behavior data and suggests appropriate products. The recommendation team recommends related products based on the user's past purchases and browsing history.

[0067] (Example of form 2) The logistics optimization system according to an embodiment of the present invention is a system that analyzes each process of logistics and proposes the optimal route and schedule. The aim of this system is to shorten logistics lead times and bring user lead times as close to zero as possible. Specifically, it comprises a logistics analysis unit that analyzes each process of logistics, a delivery route selection unit that selects a delivery route based on the analyzed information, a demand forecasting unit that analyzes users' purchase history and behavioral data, an inventory management unit that manages inventory based on the forecast results, and a recommendation unit that analyzes user behavioral data and proposes appropriate products. For example, the logistics analysis unit monitors the inventory status of products and the congestion status of delivery routes in real time and selects the optimal delivery route. This shortens the delivery time of products and reduces the time from when a user wants a product until they actually receive it. Next, the demand forecasting unit analyzes users' purchase history and behavioral data and predicts future demand. For example, it predicts how much of a particular product will be sold and when, and manages inventory appropriately. This prevents product shortages and allows users to purchase products immediately when they want them. Furthermore, the recommendation unit analyzes user behavioral data and proposes related products. For example, the system recommends related products based on the user's past purchases and browsing history. This shortens the time it takes for the user to click the purchase button, eliminating the time lag between online and offline. This system can bring logistics lead times and user lead times as close to zero as possible, eliminating the time lag between the offline and online worlds. In this way, the logistics optimization system can shorten logistics lead times and user lead times, eliminating the time lag between offline and online.

[0068] The logistics optimization system according to this embodiment comprises a logistics analysis unit, a delivery route selection unit, a demand forecasting unit, an inventory management unit, and a recommendation unit. The logistics analysis unit analyzes each process of logistics. Each process of logistics includes, but is not limited to, order processing, picking, packing, and shipping. The logistics analysis unit analyzes each process of logistics using AI, for example, and proposes the optimal route and schedule. For example, the logistics analysis unit monitors the inventory status of products and the congestion status of delivery routes in real time and selects the optimal delivery route. The delivery route selection unit selects a delivery route based on the information analyzed by the logistics analysis unit. For example, the delivery route selection unit selects the optimal delivery route based on criteria such as distance, time, and cost. The demand forecasting unit analyzes users' purchase history and behavioral data and forecasts future demand. For example, the demand forecasting unit forecasts how much of a particular product will be sold and when, and manages inventory appropriately. The demand forecasting unit analyzes users' purchase history and behavioral data using AI and forecasts future demand. For example, the demand forecasting department predicts when and how much of a particular product will sell based on users' purchase history and behavioral data. The inventory management department manages inventory based on the forecast results obtained by the demand forecasting department. The inventory management department manages inventory based on criteria such as how to set inventory levels and when to replenish. The recommendation department analyzes user behavioral data and suggests appropriate products. The recommendation department recommends related products based on products that users have previously purchased and their browsing history. As a result, the logistics optimization system can shorten logistics lead times and user lead times, and eliminate the time lag between offline and online.

[0069] The Logistics Analytics Department analyzes each process in logistics. These processes include, but are not limited to, order processing, picking, packing, and shipping. For example, the Logistics Analytics Department uses AI to analyze each process and propose optimal routes and schedules. Specifically, at the order processing stage, the department collects order data, and AI analyzes this data to determine which warehouse is most efficient for shipping which products. At the picking stage, AI optimizes product placement and picking routes, enabling workers to collect products in the shortest possible time. At the packing stage, AI proposes the optimal packing method based on the shape and size of the products, reducing waste of packaging materials. At the shipping stage, AI monitors congestion and traffic information on delivery routes in real time and proposes the optimal shipping schedule. This allows the Logistics Analytics Department to streamline each process in logistics and shorten overall lead times. Furthermore, based on historical data and trends, the Logistics Analytics Department can predict future logistics demand and take proactive measures. For example, it can predict increased demand during specific seasons or events and secure the necessary resources in advance. Furthermore, the logistics analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data early and respond quickly. This allows the logistics analysis department to contribute not only to logistics efficiency but also to risk management and anomaly response, thereby improving the overall reliability and security of the system.

[0070] The delivery route selection unit selects delivery routes based on information analyzed by the logistics analysis unit. The unit selects the optimal delivery route based on criteria such as distance, time, and cost. Specifically, it uses AI to simulate multiple delivery routes and select the most efficient one. The AI ​​analyzes traffic information, weather information, and road congestion in real time to calculate the optimal route. For example, the AI ​​can suggest a route that uses back roads instead of main roads to avoid traffic congestion. The delivery route selection unit also considers factors such as the fuel efficiency of delivery vehicles and the working hours of drivers to select the most cost-effective route. This allows the delivery route selection unit to simultaneously achieve shorter delivery times and cost reductions. Furthermore, when there are multiple delivery destinations, the delivery route selection unit can calculate the optimal delivery order and propose an efficient route. For example, the AI ​​considers the geographical location of the destinations and delivery time constraints to calculate a route that visits all destinations via the shortest distance. The delivery route selection unit can also recalculate routes based on real-time updated information, allowing it to flexibly adapt to changing circumstances. This allows the delivery route selection unit to always provide the optimal route, improving the efficiency and reliability of deliveries.

[0071] The demand forecasting department analyzes users' purchase history and behavioral data to predict future demand. For example, it predicts how much of a particular product will sell and when, and manages inventory appropriately. Specifically, the demand forecasting department uses AI to analyze users' purchase history and behavioral data to predict future demand. Based on past purchase data, search history, and browsing history, the AI ​​analyzes users' purchasing patterns and predicts how much of a particular product will sell and when. For example, the AI ​​predicts an increase in demand during a specific season or event based on past data and secures inventory in advance. Furthermore, the demand forecasting department can improve the accuracy of its demand forecasts by utilizing external data sources. For example, it can analyze weather data, economic indicators, and social media trends to identify factors that cause fluctuations in demand. This allows the demand forecasting department to make more accurate demand forecasts and prevent inventory surpluses and shortages. In addition, the demand forecasting department can continuously revise its demand forecasts based on real-time updated data to respond to the latest situations. For example, if a particular product suddenly starts selling well, the demand forecasting department immediately incorporates new data and updates the forecast results. Furthermore, the demand forecasting unit can use anomaly detection algorithms to detect unusual patterns and abnormal data early on, enabling rapid response. This allows the demand forecasting unit to always provide highly accurate demand forecasts based on the latest information, improving the efficiency and reliability of inventory management.

[0072] The Inventory Management Department manages inventory based on forecast results obtained by the Demand Forecasting Department. The Inventory Management Department manages inventory based on criteria such as how to set inventory levels and replenishment timing. Specifically, the Inventory Management Department uses AI to optimize inventory levels and achieve inventory management without surpluses or shortages. Based on forecast data provided by the Demand Forecasting Department, the AI ​​calculates the optimal inventory level for each product and proposes appropriate replenishment timing. For example, the AI ​​replenishes inventory before a particular product starts selling well and reduces inventory at the appropriate time to prevent unsold stock. The Inventory Management Department also plans the placement and movement of products to optimize the use of warehouse space. This enables the Inventory Management Department to achieve efficient warehouse operation and reduce costs. Furthermore, the Inventory Management Department can continuously monitor inventory status based on real-time updated inventory data and respond quickly as needed. For example, if a particular product suddenly starts selling well, the Inventory Management Department immediately incorporates the new data and adjusts the inventory level. The Inventory Management Department also uses anomaly detection algorithms to detect unusual patterns and abnormal data early and respond quickly. This allows the inventory management department to provide highly accurate inventory management based on the latest information at all times, preventing inventory shortages and surpluses and enabling efficient operations.

[0073] The recommendation department analyzes user behavior data and suggests appropriate products. For example, it recommends related products based on a user's past purchases and browsing history. Specifically, the recommendation department uses AI to analyze user behavior data and suggest products that are best suited to each individual user. The AI ​​analyzes the user's preferences and interests based on past purchase history, browsing history, search history, etc., and recommends related products. For example, the AI ​​suggests accessories and complementary products related to a user who has purchased a particular product. Furthermore, the recommendation department can analyze user behavior data in real time and provide timely recommendations. For example, it can instantly suggest related products while a user is browsing a particular product. In addition, the recommendation department can utilize external data sources to improve the accuracy of recommendations. For example, it can analyze social media trends and reviews on review sites to suggest popular and highly-rated products. This allows the recommendation department to provide more personalized product suggestions to users and increase their purchase intent. Furthermore, the recommendation department can collect user feedback and continuously improve the accuracy of its recommendation algorithms. For example, it can evaluate the effectiveness of recommendations and adjust the algorithms based on data showing whether users purchased the suggested products. This allows the recommendation department to always provide highly accurate product suggestions based on the latest information, thereby improving user satisfaction.

[0074] The logistics analysis department can monitor product inventory status or delivery route congestion in real time and select delivery routes. For example, the logistics analysis department can monitor product inventory status in real time. For example, the logistics analysis department monitors real-time updates of inventory levels and inventory types. The logistics analysis department can also monitor delivery route congestion in real time. For example, the logistics analysis department monitors traffic conditions and the operational status of distribution centers. This allows the logistics analysis department to select the optimal delivery route and shorten delivery times. Some or all of the above processes in the logistics analysis department may be performed using AI, for example, or not using AI. For example, the logistics analysis department can input inventory status and congestion data into AI and have the AI ​​select the optimal delivery route.

[0075] The demand forecasting unit can analyze users' purchase history and behavioral data to predict future demand. For example, the demand forecasting unit analyzes users' purchase history. For example, it analyzes data such as purchase date and time, purchased items, and purchase frequency. The demand forecasting unit can also analyze users' behavioral data. For example, it analyzes data such as website browsing history and click history. This allows the demand forecasting unit to accurately predict future demand. Some or all of the above processing in the demand forecasting unit may be performed using AI, for example, or without AI. For example, the demand forecasting unit can input purchase history and behavioral data into AI and have the AI ​​perform the prediction of future demand.

[0076] The inventory management department can appropriately manage inventory based on the forecast results obtained by the demand forecasting department. The inventory management department manages inventory based on criteria such as how to set inventory levels and when to replenish stock. For example, the inventory management department sets inventory levels based on the forecast results obtained by the demand forecasting department and replenishes inventory at the appropriate time. This allows the inventory management department to prevent stockouts and enable proper inventory management. Some or all of the above processes in the inventory management department may be performed using AI, for example, or not using AI. For example, the inventory management department can input forecast results into AI and have the AI ​​perform inventory management optimization.

[0077] The recommendation unit can analyze user behavior data and suggest products. For example, the recommendation unit analyzes user behavior data, such as website browsing history and click history. The recommendation unit can also suggest related products based on products the user has purchased in the past. For example, the recommendation unit recommends related products based on products the user has purchased in the past and their browsing history. This allows the recommendation unit to shorten the time it takes for the user to click the purchase button. Some or all of the above processes in the recommendation unit may be performed using AI, for example, or not. For example, the recommendation unit can input behavior data into AI and have the AI ​​optimize product suggestions.

[0078] The logistics analysis unit can estimate user emotions and adjust the priority of the logistics process based on the estimated user emotions. For example, the logistics analysis unit estimates user emotions using an emotion analysis algorithm. The logistics analysis unit can also adjust the priority of the logistics process based on the estimated user emotions. For example, if the user is in a hurry, the logistics analysis unit adjusts the logistics process to prioritize urgent deliveries. If the user is relaxed, the logistics analysis unit can also select a delivery route that prioritizes cost efficiency. Furthermore, if the user is feeling anxious, the logistics analysis unit can enhance its ability to notify the user of the delivery status in real time. This enables the logistics analysis unit to deliver according to the user's needs. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the logistics analysis unit may be performed using AI, for example, or without AI. For example, the logistics analysis department can input user emotion data into a generating AI and have the AI ​​perform emotion estimation.

[0079] The logistics analysis unit can select an analysis algorithm by referring to past delivery data during logistics analysis. For example, the logistics analysis unit can refer to past delivery data. For example, the logistics analysis unit can refer to data such as delivery date and time, delivery route, and delivery time. The logistics analysis unit can also apply an algorithm to select the most efficient delivery route based on past delivery data. For example, the logistics analysis unit can select the optimal delivery route for a specific season or time of day from past delivery data. The logistics analysis unit can also analyze past delivery data and select an algorithm to avoid frequently occurring problems. This allows the logistics analysis unit to select the optimal analysis algorithm and improve the accuracy of the analysis. Some or all of the above processes in the logistics analysis unit may be performed using AI, for example, or without AI. For example, the logistics analysis unit can input past delivery data into AI and have the AI ​​select the optimal analysis algorithm.

[0080] The logistics analysis department can apply different analysis methods depending on the characteristics of the product during logistics analysis. For example, in the case of fresh food, temperature control is important, so the logistics analysis department incorporates temperature sensor data into the analysis. For example, the logistics analysis department selects a delivery route for products that require temperature control based on the temperature sensor data. Furthermore, in the case of expensive products, the logistics analysis department can also apply analysis methods that prioritize security. For example, the logistics analysis department considers security risks when selecting a delivery route for expensive products. In addition, when handling a large volume of products, the logistics analysis department can apply analysis methods that perform efficient batch processing. For example, the logistics analysis department applies a batch processing algorithm to efficiently deliver a large volume of products. This allows the logistics analysis department to apply analysis methods appropriate to the characteristics of the product and improve the accuracy of the analysis. Some or all of the above processes in the logistics analysis department may be performed using AI, for example, or without AI. For example, the logistics analysis department can input product characteristic data into AI and have the AI ​​select the optimal analysis method.

[0081] The logistics analysis unit can estimate the user's emotions and adjust the display method of the logistics process based on the estimated user emotions. For example, the logistics analysis unit estimates the user's emotions using an emotion analysis algorithm. The logistics analysis unit can also adjust the display method of the logistics process based on the estimated user emotions. For example, if the user is stressed, the logistics analysis unit can provide a simple and highly visible display method. If the user is relaxed, the logistics analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the logistics analysis unit can provide a concise display method. In this way, the logistics analysis unit adjusts the display method based on the user's emotions, enabling a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the logistics analysis unit may be performed using AI, for example, or without AI. For example, the logistics analysis department can input user emotion data into a generating AI and have the AI ​​perform emotion estimation.

[0082] The logistics analysis department can improve the accuracy of its analysis based on geographical factors during logistics analysis. For example, the logistics analysis department considers geographical factors. For example, the logistics analysis department applies analysis methods that are appropriate for the characteristics of mountainous areas or urban areas. The logistics analysis department can also apply analysis methods that minimize the impact of traffic congestion and road construction. For example, the logistics analysis department selects the optimal delivery route based on traffic congestion and road construction data. Furthermore, the logistics analysis department can apply analysis methods that respond to seasonal and weather fluctuations. For example, the logistics analysis department selects the optimal delivery route based on seasonal and weather data. In this way, the logistics analysis department can improve the accuracy of its analysis by considering geographical factors. Some or all of the above processes in the logistics analysis department may be performed using AI, for example, or without AI. For example, the logistics analysis department can input geographical factor data into AI and have the AI ​​perform the analysis accuracy improvement.

[0083] The logistics analysis unit can improve the accuracy of its analysis by referring to relevant external data during logistics analysis. For example, the logistics analysis unit can refer to external traffic information data. For example, the logistics analysis unit can select the optimal delivery route based on traffic information data. The logistics analysis unit can also refer to external weather data and perform analysis that takes weather effects into account. For example, the logistics analysis unit can select a delivery route that minimizes the impact of weather based on weather data. Furthermore, the logistics analysis unit can refer to external market data and perform analysis that responds to fluctuations in demand. For example, the logistics analysis unit can predict fluctuations in demand based on market data and select the optimal delivery route. In this way, the logistics analysis unit can improve the accuracy of its analysis by referring to relevant external data. Some or all of the above processes in the logistics analysis unit may be performed using AI, for example, or without AI. For example, the logistics analysis unit can input external data into AI and have AI perform the task of improving the accuracy of the analysis.

[0084] The delivery route selection unit can estimate the user's emotions and adjust the delivery route selection criteria based on the estimated user emotions. For example, the delivery route selection unit estimates the user's emotions using an emotion analysis algorithm. The delivery route selection unit can also adjust the delivery route selection criteria based on the estimated user emotions. For example, if the user is in a hurry, the delivery route selection unit will prioritize the shortest route. If the user is relaxed, the delivery route selection unit can also select a route that prioritizes cost efficiency. Furthermore, if the user is feeling anxious, the delivery route selection unit can select a route that notifies the user of the delivery status in real time. This enables the delivery route selection unit to deliver according to the user's needs. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery route selection unit may be performed using AI, for example, or without AI. For example, the delivery route selection unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0085] The delivery route selection unit can select a route by referring to past delivery history when selecting a delivery route. For example, the delivery route selection unit can refer to past delivery history. For example, the delivery route selection unit can refer to data such as delivery date and time, delivery route, and delivery time. The delivery route selection unit can also select the most efficient route based on past delivery history. For example, the delivery route selection unit can select the optimal route for a specific season or time of day from past delivery history. The delivery route selection unit can also analyze past delivery history and select a route that avoids frequently occurring problems. In this way, the delivery route selection unit can select the optimal route and improve delivery efficiency. Some or all of the above processing in the delivery route selection unit may be performed using AI, for example, or without AI. For example, the delivery route selection unit can input past delivery history into AI and have the AI ​​perform the selection of the optimal route.

[0086] The delivery route selection unit can apply different route selection algorithms depending on the characteristics of the product when selecting a delivery route. For example, in the case of fresh food, temperature control is important, so the delivery route selection unit selects a route that takes temperature sensor data into consideration. For example, the delivery route selection unit selects a delivery route for products that require temperature control based on temperature sensor data. In addition, the delivery route selection unit can also select a route that prioritizes security in the case of expensive products. For example, the delivery route selection unit considers security risks when selecting a delivery route for expensive products. Furthermore, when handling a large volume of products, the delivery route selection unit can perform route selection that performs efficient batch processing. For example, the delivery route selection unit applies a batch processing algorithm to efficiently deliver a large volume of products. In this way, the delivery route selection unit can apply a route selection algorithm that is appropriate for the characteristics of the product and improve the accuracy of delivery. Some or all of the above processing in the delivery route selection unit may be performed using AI, for example, or without using AI. For example, the delivery route selection unit can input product characteristic data into AI and have the AI ​​execute the application of the optimal route selection algorithm.

[0087] The delivery route selection unit can estimate the user's emotions and adjust the display method of the delivery route based on the estimated user emotions. For example, the delivery route selection unit estimates the user's emotions using an emotion analysis algorithm. The delivery route selection unit can also adjust the display method of the delivery route based on the estimated user emotions. For example, if the user is stressed, the delivery route selection unit can provide a simple and highly visible display method. If the user is relaxed, the delivery route selection unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the delivery route selection unit can provide a concise display method. In this way, the delivery route selection unit adjusts the display method based on the user's emotions, enabling a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the delivery route selection unit may be performed using AI, for example, or without AI. For example, the delivery route selection unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0088] The delivery route selection unit can improve the accuracy of route selection based on geographical factors during the delivery route selection process. For example, the delivery route selection unit considers geographical factors. For example, it selects routes according to the characteristics of mountainous areas and urban areas. The delivery route selection unit can also select routes that minimize the impact of traffic congestion and road construction. For example, it selects the optimal delivery route based on traffic congestion and road construction data. Furthermore, the delivery route selection unit can select routes that respond to seasonal and weather fluctuations. For example, it selects the optimal delivery route based on seasonal and weather data. In this way, the delivery route selection unit can improve the accuracy of route selection by considering geographical factors. Some or all of the above processing in the delivery route selection unit may be performed using AI, for example, or without AI. For example, the delivery route selection unit can input geographical factor data into AI and have the AI ​​perform the route selection accuracy improvement.

[0089] The delivery route selection unit can improve the accuracy of route selection by referring to relevant external data when selecting a delivery route. For example, the delivery route selection unit can refer to external traffic information data. For example, the delivery route selection unit can select the optimal delivery route based on traffic information data. The delivery route selection unit can also refer to external weather data and select a route that takes weather effects into account. For example, the delivery route selection unit can select a delivery route that minimizes the impact of weather based on weather data. Furthermore, the delivery route selection unit can refer to external market data and select a route that responds to fluctuations in demand. For example, the delivery route selection unit can predict fluctuations in demand based on market data and select the optimal delivery route. In this way, the delivery route selection unit can improve the accuracy of route selection by referring to relevant external data. Some or all of the above processing in the delivery route selection unit may be performed using AI, for example, or without using AI. For example, the delivery route selection unit can input external data into AI and have the AI ​​perform the task of improving the accuracy of route selection.

[0090] The demand forecasting unit can estimate the user's emotions and adjust the accuracy of the demand forecast based on the estimated user emotions. For example, the demand forecasting unit estimates the user's emotions using an emotion analysis algorithm. The demand forecasting unit can also adjust the accuracy of the demand forecast based on the estimated user emotions. For example, if the user is excited, the demand forecasting unit uses detailed data to improve the accuracy of the demand forecast. If the user is relaxed, the demand forecasting unit can also adjust the accuracy of the demand forecast to prioritize cost efficiency. Furthermore, if the user is feeling anxious, the demand forecasting unit can notify the user of the demand forecast results in real time. This allows the demand forecasting unit to adjust the accuracy of the demand forecast based on the user's emotions, enabling more accurate demand forecasting. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the demand forecasting unit may be performed using AI, for example, or without AI. For example, the demand forecasting unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0091] The demand forecasting unit can select a forecasting algorithm by referring to past purchase data when forecasting demand. For example, the demand forecasting unit may refer to past purchase data such as purchase date and time, purchased items, and purchase frequency. The demand forecasting unit can also select the most accurate forecasting algorithm based on past purchase data. For example, the demand forecasting unit may select an algorithm that forecasts demand in a specific season or event from past purchase data. The demand forecasting unit may also analyze past purchase data and select an algorithm that forecasts frequently occurring fluctuations in demand. In this way, the demand forecasting unit can select the optimal forecasting algorithm and improve the accuracy of demand forecasting. Some or all of the above processing in the demand forecasting unit may be performed using AI, for example, or not using AI. For example, the demand forecasting unit can input past purchase data into AI and have the AI ​​select the optimal forecasting algorithm.

[0092] The demand forecasting unit can apply different forecasting methods depending on the characteristics of the product when forecasting demand. For example, in the case of fresh food, the demand forecasting unit can apply a demand forecasting method that takes into account the expiration date. For example, the demand forecasting unit forecasts the demand for fresh food based on the expiration date. The demand forecasting unit can also apply a demand forecasting method that takes into account the purchase frequency in the case of expensive products. For example, the demand forecasting unit forecasts the demand based on the purchase frequency of expensive products. Furthermore, when dealing with a large volume of products, the demand forecasting unit can also apply a demand forecasting method that takes batch processing into account. For example, the demand forecasting unit can apply a batch processing algorithm to efficiently manage a large volume of products. This allows the demand forecasting unit to apply forecasting methods appropriate to the characteristics of the product and improve the accuracy of demand forecasting. Some or all of the above processing in the demand forecasting unit may be performed using AI, for example, or without AI. For example, the demand forecasting unit can input product characteristic data into AI and have the AI ​​apply the optimal forecasting method.

[0093] The demand forecasting unit can estimate the user's emotions and adjust the display method of the demand forecast based on the estimated user emotions. For example, the demand forecasting unit estimates the user's emotions using an emotion analysis algorithm. The demand forecasting unit can also adjust the display method of the demand forecast based on the estimated user emotions. For example, if the user is stressed, the demand forecasting unit can provide a simple and highly visible display method. If the user is relaxed, the demand forecasting unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the demand forecasting unit can provide a concise display method. In this way, the demand forecasting unit adjusts the display method based on the user's emotions, enabling a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the demand forecasting unit may be performed using AI, for example, or without AI. For example, the demand forecasting unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0094] The demand forecasting unit can improve the accuracy of its forecasts based on geographical factors during the demand forecasting process. For example, the demand forecasting unit considers geographical factors. For example, the demand forecasting unit applies a method for forecasting demand in a specific region. The demand forecasting unit can also apply demand forecasting methods that respond to seasonal and weather fluctuations. For example, the demand forecasting unit forecasts demand in a specific region based on seasonal and weather data. Furthermore, the demand forecasting unit can apply a method for analyzing purchasing patterns by region. For example, the demand forecasting unit forecasts demand based on purchasing data by region. This allows the demand forecasting unit to improve the accuracy of its forecasts by considering geographical factors. Some or all of the above processing in the demand forecasting unit may be performed using AI, for example, or without AI. For example, the demand forecasting unit can input geographical factor data into AI and have AI perform the task of improving the accuracy of the forecast.

[0095] The demand forecasting unit can improve the accuracy of its forecasts by referring to relevant external data during the demand forecasting process. For example, the demand forecasting unit may refer to external market data. For example, the demand forecasting unit may make forecasts that respond to fluctuations in demand based on market data. The demand forecasting unit can also refer to external economic data and make demand forecasts that correspond to economic conditions. For example, the demand forecasting unit may make demand forecasts that correspond to economic conditions based on economic data. Furthermore, the demand forecasting unit may refer to external consumer data and analyze consumer purchasing patterns. For example, the demand forecasting unit may analyze consumer purchasing patterns based on consumer data and forecast demand. In this way, the demand forecasting unit can improve the accuracy of its forecasts by referring to relevant external data. Some or all of the above processes in the demand forecasting unit may be performed using AI, for example, or not using AI. For example, the demand forecasting unit may input external data into AI and have AI perform the task of improving the accuracy of the forecast.

[0096] The inventory management department can estimate user emotions and adjust inventory management priorities based on the estimated emotions. For example, the inventory management department can estimate user emotions using an emotion analysis algorithm. The inventory management department can also adjust inventory management priorities based on the estimated emotions. For example, if the user is in a hurry, the inventory management department will prioritize emergency inventory management. If the user is relaxed, the inventory management department can also prioritize cost-effective inventory management. Furthermore, if the user is feeling anxious, the inventory management department can enhance its ability to notify users of inventory status in real time. This allows the inventory management department to adjust inventory management priorities based on user emotions and enable inventory management that meets user needs. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processes in the inventory management department may be performed using AI, for example, or without AI. For example, the inventory management department can input user emotion data into a generative AI and have the AI ​​perform emotion estimation.

[0097] The inventory management department can select inventory management methods by referring to historical inventory data during inventory management. For example, the inventory management department can refer to historical inventory data such as inventory levels, replenishment timing, and inventory turnover rate. The inventory management department can also select the most efficient inventory management method based on historical inventory data. For example, the inventory management department can select inventory management methods for specific seasons or events from historical inventory data. The inventory management department can also analyze historical inventory data and select management methods to avoid frequent inventory shortages. This allows the inventory management department to select the optimal management method and improve the accuracy of inventory management. Some or all of the above processes in the inventory management department may be performed using AI, for example, or not. For example, the inventory management department can input historical inventory data into AI and have the AI ​​select the optimal management method.

[0098] The inventory management department can apply different management methods depending on the characteristics of the product when managing inventory. For example, in the case of fresh food, the inventory management department can apply an inventory management method that takes expiration dates into consideration. For example, the inventory management department manages the inventory of fresh food based on its expiration date. The inventory management department can also apply an inventory management method that prioritizes security when dealing with expensive products. For example, the inventory management department considers security risks when managing the inventory of expensive products. Furthermore, when handling large quantities of products, the inventory management department can apply an inventory management method that performs efficient batch processing. For example, the inventory management department can apply a batch processing algorithm to efficiently manage large quantities of products. This allows the inventory management department to apply management methods tailored to the characteristics of the product and improve the accuracy of inventory management. Some or all of the above processes in the inventory management department may be performed using AI, for example, or not. For example, the inventory management department can input product characteristic data into AI and have the AI ​​execute the application of the optimal management method.

[0099] The inventory management unit can estimate the user's emotions and adjust the way inventory management is displayed based on the estimated emotions. For example, the inventory management unit can estimate the user's emotions using an emotion analysis algorithm. The inventory management unit can also adjust the way inventory management is displayed based on the estimated emotions. For example, if the user is stressed, the inventory management unit can provide a simple and highly visible display. If the user is relaxed, the inventory management unit can also provide a display that includes detailed information. Furthermore, if the user is in a hurry, the inventory management unit can provide a concise display. This allows the inventory management unit to adjust the display based on the user's emotions, making it easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the inventory management unit may be performed using AI, for example, or without AI. For example, the inventory management department can input user emotion data into a generative AI and have the AI ​​perform emotion estimation.

[0100] The inventory management department can improve the accuracy of its inventory management based on geographical factors. For example, the inventory management department can consider geographical factors. For example, the inventory management department can apply inventory management methods specific to a particular region. The inventory management department can also apply inventory management methods that respond to seasonal and weather fluctuations. For example, the inventory management department can manage inventory in a particular region based on seasonal and weather data. Furthermore, the inventory management department can apply methods to analyze regional demand patterns. For example, the inventory management department can manage inventory based on regional demand data. This allows the inventory management department to improve the accuracy of its management by considering geographical factors. Some or all of the above processes in the inventory management department may be performed using AI, for example, or not. For example, the inventory management department can input geographical factor data into AI and have the AI ​​perform the improvement of management accuracy.

[0101] The inventory management department can improve the accuracy of its inventory management by referring to relevant external data. For example, the inventory management department can refer to external market data. For example, the inventory management department can perform inventory management in response to fluctuations in demand based on market data. The inventory management department can also refer to external economic data and perform inventory management according to economic conditions. For example, the inventory management department can perform inventory management according to economic conditions based on economic data. Furthermore, the inventory management department can refer to external consumer data and analyze consumer purchasing patterns. For example, the inventory management department can analyze consumer purchasing patterns based on consumer data and manage inventory accordingly. In this way, the inventory management department can improve the accuracy of its management by referring to relevant external data. Some or all of the above processes in the inventory management department may be performed using AI, for example, or not using AI. For example, the inventory management department can input external data into AI and have the AI ​​perform the improvement of management accuracy.

[0102] The recommendation system can estimate the user's emotions and adjust the recommendation priority based on the estimated emotions. For example, the recommendation system might estimate the user's emotions using an emotion analysis algorithm. Furthermore, the recommendation system can adjust the recommendation priority based on the estimated emotions. For example, if the user is excited, the recommendation system might prioritize recommending visually stimulating products. Similarly, if the user is relaxed, the recommendation system might prioritize recommending products with relaxing effects. Additionally, if the user is feeling anxious, the recommendation system might prioritize recommending products that provide a sense of security. This allows the recommendation system to adjust the recommendation priority based on the user's emotions, enabling product suggestions tailored to the user's needs. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0103] The recommendation unit can select a recommendation algorithm by referring to past purchase data when making recommendations. For example, the recommendation unit may refer to past purchase data such as purchase date and time, purchased items, and purchase frequency. The recommendation unit can also select the most accurate recommendation algorithm based on past purchase data. For example, the recommendation unit may select the optimal recommendation algorithm for a specific season or event from past purchase data. The recommendation unit can also analyze past purchase data and select a recommendation algorithm for frequently purchased items. This allows the recommendation unit to select the optimal recommendation algorithm and improve the accuracy of recommendations. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input past purchase data into AI and have the AI ​​select the optimal recommendation algorithm.

[0104] The recommendation unit can apply different recommendation methods depending on the characteristics of the product during the recommendation process. For example, in the case of fresh food, the recommendation unit can apply a recommendation method that takes the expiration date into consideration. For example, the recommendation unit can recommend fresh food based on its expiration date. The recommendation unit can also apply a recommendation method that takes the purchase frequency into consideration for expensive products. For example, the recommendation unit can make recommendations based on the purchase frequency of expensive products. Furthermore, when handling a large number of products, the recommendation unit can apply a recommendation method that takes batch processing into consideration. For example, the recommendation unit can apply a batch processing algorithm to efficiently manage a large number of products. This allows the recommendation unit to apply a recommendation method that is appropriate to the characteristics of the product and improve the accuracy of recommendations. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without using AI. For example, the recommendation system can input product characteristic data into the AI ​​and have the AI ​​apply the most suitable recommendation method.

[0105] The recommendation section can estimate the user's emotions and adjust the way recommendations are displayed based on the estimated emotions. For example, the recommendation section might estimate the user's emotions using an emotion analysis algorithm. Furthermore, the recommendation section can adjust the way recommendations are displayed based on the estimated emotions. For example, if the user is stressed, the recommendation section might provide a simple and highly visible display. If the user is relaxed, it might provide a display that includes detailed information. Additionally, if the user is in a hurry, it might provide a concise display. This allows the recommendation section to adjust the display based on the user's emotions, resulting in a user-friendly presentation. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0106] The recommendation unit can improve the accuracy of its recommendations based on geographical factors. For example, the recommendation unit considers geographical factors. For example, the recommendation unit applies a method to predict demand in a specific region. The recommendation unit can also apply recommendation methods that respond to seasonal and weather fluctuations. For example, the recommendation unit predicts demand in a specific region based on seasonal and weather data. Furthermore, the recommendation unit can apply a method to analyze purchasing patterns by region. For example, the recommendation unit predicts demand based on purchasing data by region. This allows the recommendation unit to improve the accuracy of its recommendations by considering geographical factors. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input geographical factor data into AI and have the AI ​​perform the recommendation accuracy improvement.

[0107] The recommendation unit can improve the accuracy of its recommendations by referring to relevant external data during the recommendation process. For example, the recommendation unit may refer to external market data. For example, the recommendation unit may make recommendations that respond to fluctuations in demand based on market data. The recommendation unit can also refer to external economic data and make recommendations that are appropriate to the economic situation. For example, the recommendation unit may make recommendations that are appropriate to the economic situation based on economic data. Furthermore, the recommendation unit may refer to external consumer data and analyze consumer purchasing patterns. For example, the recommendation unit may analyze consumer purchasing patterns based on consumer data and make recommendations. This allows the recommendation unit to improve the accuracy of its recommendations by referring to relevant external data. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit may input external data into AI and have the AI ​​perform the task of improving the accuracy of its recommendations.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The logistics analysis department can apply analytical methods to minimize energy consumption when analyzing logistics processes. For example, it can monitor the energy consumption of each delivery route in real time and select the most energy-efficient route. Furthermore, it can optimize delivery schedules based on energy consumption data. In addition, it can propose new delivery methods aimed at reducing energy consumption. Through these efforts, the logistics analysis department can minimize energy consumption and reduce its environmental impact.

[0110] The demand forecasting unit can estimate the user's emotions and adjust the timing of demand forecasts based on those emotions. For example, if the user is excited, the demand forecasting unit will increase the frequency of demand forecasts. Conversely, if the user is relaxed, the demand forecasting unit can decrease the frequency of demand forecasts. Furthermore, if the user is feeling anxious, the demand forecasting unit can notify the user of the forecast results in real time. This allows the demand forecasting unit to adjust the timing of demand forecasts based on the user's emotions, enabling more accurate demand forecasts.

[0111] The inventory management department can apply management methods that consider the product lifecycle when managing inventory. For example, the inventory management department can set inventory levels based on product lifecycle data from manufacturing to disposal. Furthermore, the inventory management department can adjust replenishment timing according to the product lifecycle stage. In addition, the inventory management department can optimize inventory based on product lifecycle data. This enables the inventory management department to manage inventory while considering the product lifecycle.

[0112] The recommendation system can estimate the user's emotions and personalize recommendations based on those emotions. For example, if the user is excited, the recommendation system will recommend highly entertaining products. It can also recommend relaxing products if the user is relaxed. Furthermore, if the user is feeling anxious, it can recommend products that provide a sense of security. This allows the recommendation system to personalize recommendations based on the user's emotions and offer product suggestions tailored to the user's needs.

[0113] The Logistics Analysis Department can apply analytical methods that take environmental data into consideration when analyzing logistics processes. For example, the Logistics Analysis Department can monitor the environmental impact of each delivery route in real time and select routes with less environmental impact. Furthermore, the Logistics Analysis Department can optimize delivery schedules based on environmental data. In addition, the Logistics Analysis Department can propose new delivery methods aimed at reducing environmental impact. In this way, the Logistics Analysis Department can reduce environmental impact by applying analytical methods that take environmental data into consideration.

[0114] The demand forecasting unit can estimate the user's emotions during demand forecasting and adjust the notification method of the forecast results based on the estimated user emotions. For example, if the user is stressed, the demand forecasting unit can provide a simple and highly visible notification method. If the user is relaxed, it can also provide a notification method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a notification method that gets straight to the point. In this way, the demand forecasting unit can adjust the notification method based on the user's emotions, enabling notifications that are easy for the user to understand.

[0115] The inventory management department can apply management methods that take into account product consumption patterns when managing inventory. For example, the inventory management department can set inventory levels based on product consumption pattern data. Furthermore, the inventory management department can adjust replenishment timing according to product consumption patterns. In addition, the inventory management department can optimize inventory based on product consumption pattern data. This enables the inventory management department to manage inventory while considering product consumption patterns.

[0116] The recommendation system can estimate the user's emotions and adjust the timing of recommendations based on those emotions. For example, if the user is excited, the recommendation system will increase the frequency of recommendations. Conversely, if the user is relaxed, the recommendation system can decrease the frequency of recommendations. Furthermore, if the user is feeling anxious, the recommendation system can notify the user of the recommendation results in real time. This allows the recommendation system to adjust the timing of recommendations based on the user's emotions, enabling more effective product suggestions.

[0117] The logistics analysis department can apply analytical methods that take cost data into consideration when analyzing logistics processes. For example, the logistics analysis department can monitor the cost of each delivery route in real time and select the most cost-effective route. Furthermore, the logistics analysis department can optimize delivery schedules based on cost data. In addition, the logistics analysis department can propose new delivery methods aimed at cost reduction. In this way, the logistics analysis department can improve cost efficiency by applying analytical methods that take cost data into consideration.

[0118] The demand forecasting unit can estimate the user's emotions during demand forecasting and adjust the display method of the forecast results based on the estimated user emotions. For example, if the user is stressed, the demand forecasting unit can provide a simple and highly visible display method. If the user is relaxed, it can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. In this way, the demand forecasting unit adjusts the display method based on the user's emotions, enabling a display that is easy for the user to understand.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The Logistics Analysis Department analyzes each process in logistics. These processes include order processing, picking, packing, and shipping. The Logistics Analysis Department uses AI to analyze each process and propose the optimal route and schedule. For example, it monitors product inventory status and delivery route congestion in real time to select the optimal delivery route. Step 2: The delivery route selection unit selects a delivery route based on the information analyzed by the logistics analysis unit. The delivery route selection unit selects the optimal delivery route based on criteria such as distance, time, and cost. Step 3: The demand forecasting unit analyzes users' purchase history and behavioral data to predict future demand. The demand forecasting unit predicts when and how much of a specific product will sell, providing information for proper inventory management. AI is used to analyze users' purchase history and behavioral data to predict future demand. Step 4: The Inventory Management Department manages inventory based on the forecast results obtained by the Demand Forecasting Department. The Inventory Management Department manages inventory based on criteria such as how to set inventory levels and when to replenish. Step 5: The recommendation team analyzes user behavior data and suggests appropriate products. The recommendation team recommends related products based on the user's past purchases and browsing history.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0123] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] For example, each of the multiple elements, including the logistics analysis unit, delivery route selection unit, demand forecasting unit, inventory management unit, and recommendation unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the logistics analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and monitors the inventory status of products and the congestion status of delivery routes in real time. The delivery route selection unit is implemented by the specific processing unit 290 of the data processing unit 12 and selects the optimal delivery route. The demand forecasting unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's purchase history and behavioral data. The inventory management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages inventory based on the forecast results. The recommendation unit is implemented by the control unit 46A of the smart device 14 and analyzes the user's behavioral data and suggests relevant products. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] For example, each of the multiple elements, including the logistics analysis unit, delivery route selection unit, demand forecasting unit, inventory management unit, and recommendation unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the logistics analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and monitors the inventory status of products and the congestion status of delivery routes in real time. The delivery route selection unit is implemented by the specific processing unit 290 of the data processing unit 12 and selects the optimal delivery route. The demand forecasting unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's purchase history and behavioral data. The inventory management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages inventory based on the forecast results. The recommendation unit is implemented by the control unit 46A of the smart glasses 214 and analyzes the user's behavioral data and suggests relevant products. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] For example, each of the multiple elements, including the logistics analysis unit, delivery route selection unit, demand forecasting unit, inventory management unit, and recommendation unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the logistics analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and monitors the inventory status of products and the congestion status of delivery routes in real time. The delivery route selection unit is implemented by the specific processing unit 290 of the data processing unit 12 and selects the optimal delivery route. The demand forecasting unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's purchase history and behavioral data. The inventory management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages inventory based on the forecast results. The recommendation unit is implemented by the control unit 46A of the headset terminal 314 and analyzes the user's behavioral data and suggests relevant products. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0165] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] For example, each of the multiple elements, including the logistics analysis unit, delivery route selection unit, demand forecasting unit, inventory management unit, and recommendation unit, is implemented by at least one of the robot 414 and the data processing unit 12. For example, the logistics analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and monitors the inventory status of products and the congestion status of delivery routes in real time. The delivery route selection unit is implemented by the specific processing unit 290 of the data processing unit 12 and selects the optimal delivery route. The demand forecasting unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's purchase history and behavioral data. The inventory management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages inventory based on the forecast results. The recommendation unit is implemented by the control unit 46A of the robot 414 and analyzes the user's behavioral data and suggests relevant products. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0174] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0176] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0177] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0178] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0182] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0183] 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.

[0184] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0185] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0186] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0187] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0189] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0190] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0191] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0192] (Note 1) The Logistics Analysis Department analyzes each process in logistics, A delivery route selection unit selects a delivery route based on the information analyzed by the aforementioned logistics analysis unit, A demand forecasting unit that analyzes the user's purchase history or behavioral data, An inventory management unit manages inventory based on the forecast results obtained by the demand forecasting unit, It includes a recommendation unit that analyzes user behavior data and suggests products. A system characterized by the following features. (Note 2) The aforementioned logistics analysis unit, The system monitors product inventory status and delivery route congestion in real time to select the most suitable delivery route. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned demand forecasting unit, We analyze users' purchase history and behavioral data to predict future demand. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned inventory management department, Inventory is appropriately managed based on the forecast results obtained by the aforementioned demand forecasting unit. The system described in Appendix 1, characterized by the features described herein. (Note 5) The recommendation unit is, We analyze user behavior data and suggest products. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned logistics analysis unit, It estimates user emotions and adjusts logistics process priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned logistics analysis unit, During logistics analysis, the analysis algorithm is selected by referring to past delivery data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned logistics analysis unit, When analyzing logistics, different analytical methods are applied depending on the characteristics of the product. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned logistics analysis unit, It estimates user sentiment and adjusts how the logistics process is displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned logistics analysis unit, When analyzing logistics, improve the accuracy of the analysis based on geographical factors. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned logistics analysis unit, When performing logistics analysis, referencing relevant external data improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned delivery route selection unit is: The system estimates user emotions and adjusts delivery route selection criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned delivery route selection unit is: When selecting a delivery route, refer to past delivery history to choose the best route. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned delivery route selection unit is: When selecting delivery routes, different route selection algorithms are applied depending on the characteristics of the product. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned delivery route selection unit is: The system estimates the user's emotions and adjusts how delivery routes are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned delivery route selection unit is: When selecting delivery routes, improve the accuracy of route selection based on geographical factors. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned delivery route selection unit is: When selecting delivery routes, we improve the accuracy of route selection by referring to relevant external data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned demand forecasting unit, It estimates user sentiment and adjusts the accuracy of demand forecasts based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned demand forecasting unit, When forecasting demand, the forecasting algorithm is selected by referring to past purchasing data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned demand forecasting unit, When forecasting demand, different forecasting methods are applied depending on the characteristics of the product. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned demand forecasting unit, It estimates user sentiment and adjusts how demand forecasts are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned demand forecasting unit, When forecasting demand, improve forecast accuracy based on geographical factors. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned demand forecasting unit, When forecasting demand, referencing relevant external data improves the accuracy of the forecast. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned inventory management department, It estimates user sentiment and adjusts inventory management priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned inventory management department, When managing inventory, select a management method by referring to past inventory data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned inventory management department, When managing inventory, different management methods are applied depending on the characteristics of the product. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned inventory management department, It estimates the user's emotions and adjusts the way inventory management is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned inventory management department, Improve the accuracy of inventory management based on geographical factors. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned inventory management department, When managing inventory, referencing relevant external data improves the accuracy of management. The system described in Appendix 1, characterized by the features described herein. (Note 30) The recommendation unit is, It estimates the user's emotions and adjusts the recommendation priority based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The recommendation unit is, When making recommendations, the recommendation algorithm is selected by referring to past purchase data. The system described in Appendix 1, characterized by the features described herein. (Note 32) The recommendation unit is, When making recommendations, different recommendation methods are applied depending on the characteristics of the product. The system described in Appendix 1, characterized by the features described herein. (Note 33) The recommendation unit is, It estimates the user's emotions and adjusts how recommendations are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The recommendation unit is, When making recommendations, improve the accuracy of recommendations based on geographical factors. The system described in Appendix 1, characterized by the features described herein. (Note 35) The recommendation unit is, When making recommendations, we refer to relevant external data to improve the accuracy of the recommendations. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The Logistics Analysis Department analyzes each process in logistics, A delivery route selection unit selects a delivery route based on the information analyzed by the aforementioned logistics analysis unit, A demand forecasting unit that analyzes the user's purchase history or behavioral data, An inventory management unit manages inventory based on the forecast results obtained by the demand forecasting unit, It includes a recommendation unit that analyzes user behavior data and suggests products. A system characterized by the following features.

2. The aforementioned logistics analysis unit, The system monitors product inventory status and delivery route congestion in real time to select the most suitable delivery route. The system according to feature 1.

3. The aforementioned demand forecasting unit, We analyze users' purchase history and behavioral data to predict future demand. The system according to feature 1.

4. The aforementioned inventory management department, Inventory is appropriately managed based on the forecast results obtained by the aforementioned demand forecasting unit. The system according to feature 1.

5. The recommendation unit is, We analyze user behavior data and suggest products. The system according to feature 1.

6. The aforementioned logistics analysis unit, It estimates user emotions and adjusts logistics process priorities based on those estimated emotions. The system according to feature 1.

7. The aforementioned logistics analysis unit, During logistics analysis, the analysis algorithm is selected by referring to past delivery data. The system according to feature 1.

8. The aforementioned logistics analysis unit, When analyzing logistics, different analytical methods are applied depending on the characteristics of the product. The system according to feature 1.

9. The aforementioned logistics analysis unit, It estimates user emotions and adjusts how the logistics process is displayed based on those estimated emotions. The system according to feature 1.

10. The aforementioned logistics analysis unit, When analyzing logistics, improve the accuracy of the analysis based on geographical factors. The system according to feature 1.

Images